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overflow: hidden; text-overflow: ellipsis; -webkit-line-clamp: 3; -webkit-box-orient: vertical; }</style><div class="col-xs-12 clearfix"><div class="u-floatLeft"><h1 class="PageHeader-title u-m0x u-fs30">Financial time series</h1><div class="u-tcGrayDark">184 Followers</div><div class="u-tcGrayDark u-mt2x">Recent papers in <b>Financial time series</b></div></div></div></div></div></div><div class="TabbedNavigation"><div class="container"><div class="row"><div class="col-xs-12 clearfix"><ul class="nav u-m0x u-p0x list-inline u-displayFlex"><li class="active"><a href="https://www.academia.edu/Documents/in/Financial_time_series">Top Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Financial_time_series/MostCited">Most Cited Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Financial_time_series/MostDownloaded">Most Downloaded Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Financial_time_series/MostRecent">Newest Papers</a></li><li><a class="" href="https://www.academia.edu/People/Financial_time_series">People</a></li></ul></div><style type="text/css">ul.nav{flex-direction:row}@media(max-width: 567px){ul.nav{flex-direction:column}.TabbedNavigation li{max-width:100%}.TabbedNavigation li.active{background-color:var(--background-grey, #dddde2)}.TabbedNavigation li.active:before,.TabbedNavigation li.active:after{display:none}}</style></div></div></div><div class="container"><div class="row"><div class="col-xs-12"><div class="u-displayFlex"><div class="u-flexGrow1"><div class="works"><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_66593840" data-work_id="66593840" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/66593840/Discrimination_between_deterministic_trend_and_stochastic_trend_processes">Discrimination between deterministic trend and stochastic trend processes</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Most of economic and financial time series have a nonstationary behavior. There are different types of nonstationary processes, such as those with stochastic trend and those with deterministic trend. In practice, it can be quite difficult... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_66593840" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Most of economic and financial time series have a nonstationary behavior. There are different types of nonstationary processes, such as those with stochastic trend and those with deterministic trend. In practice, it can be quite difficult to distinguish between the two processes. In this paper, we compare random walk and determinist trend processes using sample autocorrelation, sample partial autocorrelation and periodogram based metrics.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/66593840" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c006d166d426c0ee71a541cdbaaad799" rel="nofollow" data-download="{"attachment_id":77723429,"asset_id":66593840,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/77723429/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="44861136" href="https://ulisboa.academia.edu/NunoCrato">Nuno Crato</a><script data-card-contents-for-user="44861136" type="text/json">{"id":44861136,"first_name":"Nuno","last_name":"Crato","domain_name":"ulisboa","page_name":"NunoCrato","display_name":"Nuno Crato","profile_url":"https://ulisboa.academia.edu/NunoCrato?f_ri=61227","photo":"https://0.academia-photos.com/44861136/20252214/19967244/s65_nuno.crato.jpg"}</script></span></span></li><li class="js-paper-rank-work_66593840 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="66593840"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 66593840, container: ".js-paper-rank-work_66593840", }); 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$(".js-view-count[data-work-id=66593840]").text(description); $(".js-view-count-work_66593840").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_66593840").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="66593840"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">9</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="300" rel="nofollow" href="https://www.academia.edu/Documents/in/Mathematics">Mathematics</a>, <script data-card-contents-for-ri="300" type="text/json">{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4456" rel="nofollow" href="https://www.academia.edu/Documents/in/Time_Series">Time Series</a>, <script data-card-contents-for-ri="4456" type="text/json">{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="61227" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_time_series">Financial time series</a>, <script data-card-contents-for-ri="61227" type="text/json">{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="78086" rel="nofollow" href="https://www.academia.edu/Documents/in/Random_Walk">Random Walk</a><script data-card-contents-for-ri="78086" type="text/json">{"id":78086,"name":"Random Walk","url":"https://www.academia.edu/Documents/in/Random_Walk?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=66593840]'), work: {"id":66593840,"title":"Discrimination between deterministic trend and stochastic trend processes","created_at":"2021-12-30T09:29:15.328-08:00","url":"https://www.academia.edu/66593840/Discrimination_between_deterministic_trend_and_stochastic_trend_processes?f_ri=61227","dom_id":"work_66593840","summary":"Most of economic and financial time series have a nonstationary behavior. There are different types of nonstationary processes, such as those with stochastic trend and those with deterministic trend. In practice, it can be quite difficult to distinguish between the two processes. 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A financial series is decomposed into an over complete, shift... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_68452262" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this paper, we investigate the effectiveness of a financial time-series forecasting strategy which exploits the mul- tiresolution property of the wavelet transform. A financial series is decomposed into an over complete, shift invariant scale-related representation. In transform space, each individual wavelet series is modeled by a separate multilayer perceptron (MLP). To better utilize the detailed information in the lower scales of wavelet coef- ficients (high frequencies) and general (trend) information in the higher scales of wavelet coefficients (low frequencies), we applied the Bayesian method of automatic relevance determination (ARD) to choose short past windows (short-term history) for the inputs to the MLPs at lower scales and long past windows (long-term history) at higher scales. To form the overall forecast, the indi- vidual forecasts are then recombined by the linear reconstruction property of the inverse transform with the chosen autocorrelation shell representatio...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/68452262" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="561e02342e52a584a4554238d1e7e416" rel="nofollow" data-download="{"attachment_id":78921807,"asset_id":68452262,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/78921807/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="146422969" href="https://independent.academia.edu/BarryFlower">Barry Flower</a><script data-card-contents-for-user="146422969" type="text/json">{"id":146422969,"first_name":"Barry","last_name":"Flower","domain_name":"independent","page_name":"BarryFlower","display_name":"Barry Flower","profile_url":"https://independent.academia.edu/BarryFlower?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_68452262 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="68452262"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 68452262, container: ".js-paper-rank-work_68452262", }); 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A financial series is decomposed into an over complete, shift invariant scale-related representation. In transform space, each individual wavelet series is modeled by a separate multilayer perceptron (MLP). To better utilize the detailed information in the lower scales of wavelet coef- ficients (high frequencies) and general (trend) information in the higher scales of wavelet coefficients (low frequencies), we applied the Bayesian method of automatic relevance determination (ARD) to choose short past windows (short-term history) for the inputs to the MLPs at lower scales and long past windows (long-term history) at higher scales. To form the overall forecast, the indi- vidual forecasts are then recombined by the linear reconstruction property of the inverse transform with the chosen autocorrelation shell representatio...","downloadable_attachments":[{"id":78921807,"asset_id":68452262,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":146422969,"first_name":"Barry","last_name":"Flower","domain_name":"independent","page_name":"BarryFlower","display_name":"Barry Flower","profile_url":"https://independent.academia.edu/BarryFlower?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":128,"name":"History","url":"https://www.academia.edu/Documents/in/History?f_ri=61227","nofollow":true},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=61227","nofollow":true},{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true},{"id":11598,"name":"Neural Networks","url":"https://www.academia.edu/Documents/in/Neural_Networks?f_ri=61227","nofollow":true},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine?f_ri=61227"},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary?f_ri=61227"},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227"},{"id":91262,"name":"Wavelet Transform","url":"https://www.academia.edu/Documents/in/Wavelet_Transform?f_ri=61227"},{"id":91365,"name":"Wavelet Transforms","url":"https://www.academia.edu/Documents/in/Wavelet_Transforms?f_ri=61227"},{"id":109147,"name":"Bayesian methods","url":"https://www.academia.edu/Documents/in/Bayesian_methods?f_ri=61227"},{"id":191344,"name":"Autocorrelation","url":"https://www.academia.edu/Documents/in/Autocorrelation?f_ri=61227"},{"id":192234,"name":"Wavelet Decomposition","url":"https://www.academia.edu/Documents/in/Wavelet_Decomposition?f_ri=61227"},{"id":204472,"name":"Predictive models","url":"https://www.academia.edu/Documents/in/Predictive_models?f_ri=61227"},{"id":238159,"name":"Multilayer Perceptron","url":"https://www.academia.edu/Documents/in/Multilayer_Perceptron?f_ri=61227"},{"id":290799,"name":"Management System","url":"https://www.academia.edu/Documents/in/Management_System?f_ri=61227"},{"id":362778,"name":"Shift Invariant","url":"https://www.academia.edu/Documents/in/Shift_Invariant?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_18721118 coauthored" data-work_id="18721118" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/18721118/Tracing_the_temporal_evolution_of_clusters_in_a_financial_stock_market">Tracing the temporal evolution of clusters in a financial stock market</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We propose a methodology for clustering financial time series of stocks&#39; returns, and a graphical set-up to quantify and visualise the evolution of these clusters through time. The proposed graphical representation allows for the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_18721118" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We propose a methodology for clustering financial time series of stocks&#39; returns, and a graphical set-up to quantify and visualise the evolution of these clusters through time. The proposed graphical representation allows for the application of well known algorithms for solving classical combinatorial graph problems, which can be interpreted as problems relevant to portfolio design and investment strategies. We illustrate</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/18721118" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="fd5ec70d2bb7e4af79261322df587d04" rel="nofollow" data-download="{"attachment_id":40219082,"asset_id":18721118,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/40219082/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="38788733" href="https://independent.academia.edu/AlejandraCaba%C3%B1a">Alejandra Cabaña</a><script data-card-contents-for-user="38788733" type="text/json">{"id":38788733,"first_name":"Alejandra","last_name":"Cabaña","domain_name":"independent","page_name":"AlejandraCabaña","display_name":"Alejandra Cabaña","profile_url":"https://independent.academia.edu/AlejandraCaba%C3%B1a?f_ri=61227","photo":"https://0.academia-photos.com/38788733/137242333/126701778/s65_alejandra.caba_a.jpeg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-18721118">+1</span><div class="hidden js-additional-users-18721118"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/ArgimiroArratia">Argimiro Arratia</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-18721118'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-18721118').html(); 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returns, and a graphical set-up to quantify and visualise the evolution of these clusters through time. The proposed graphical representation allows for the application of well known algorithms for solving classical combinatorial graph problems, which can be interpreted as problems relevant to portfolio design and investment strategies. We illustrate","downloadable_attachments":[{"id":40219082,"asset_id":18721118,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":38788733,"first_name":"Alejandra","last_name":"Cabaña","domain_name":"independent","page_name":"AlejandraCabaña","display_name":"Alejandra Cabaña","profile_url":"https://independent.academia.edu/AlejandraCaba%C3%B1a?f_ri=61227","photo":"https://0.academia-photos.com/38788733/137242333/126701778/s65_alejandra.caba_a.jpeg"},{"id":39197368,"first_name":"Argimiro","last_name":"Arratia","domain_name":"independent","page_name":"ArgimiroArratia","display_name":"Argimiro Arratia","profile_url":"https://independent.academia.edu/ArgimiroArratia?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":725,"name":"Computational Economics","url":"https://www.academia.edu/Documents/in/Computational_Economics?f_ri=61227","nofollow":true},{"id":747,"name":"Econometrics","url":"https://www.academia.edu/Documents/in/Econometrics?f_ri=61227","nofollow":true},{"id":29156,"name":"Stock Market","url":"https://www.academia.edu/Documents/in/Stock_Market?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true},{"id":161591,"name":"Graph Representation","url":"https://www.academia.edu/Documents/in/Graph_Representation?f_ri=61227"},{"id":311931,"name":"STOCK EXCHANGE","url":"https://www.academia.edu/Documents/in/STOCK_EXCHANGE?f_ri=61227"},{"id":639625,"name":"Investment Strategies","url":"https://www.academia.edu/Documents/in/Investment_Strategies?f_ri=61227"},{"id":1011634,"name":"Graphical Representation","url":"https://www.academia.edu/Documents/in/Graphical_Representation?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_69900673" data-work_id="69900673" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/69900673/Financial_Prediction_and_Trading_Strategies_Using">Financial Prediction and Trading Strategies Using</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Neuro[uzzy approaches for predicting financial time series are investigated and shown to perfarm well in the context of various trading strategies. The horizon of prediction is typically a few days and trading strategies are examined... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_69900673" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Neuro[uzzy approaches for predicting financial time series are investigated and shown to perfarm well in the context of various trading strategies. The horizon of prediction is typically a few days and trading strategies are examined using historical data. A methodology is presented where neurnl predictors are used to anticipate the general behavior of financial indices (moving up, down, or staying cOJlstant) in the context of stocks and options trading. The methodology is tested with actual financial data and shows considerable promise as a decision making and planning tool.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/69900673" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="46375718" href="https://iwu.academia.edu/MBrun">M. Brün</a><script data-card-contents-for-user="46375718" type="text/json">{"id":46375718,"first_name":"M.","last_name":"Brün","domain_name":"iwu","page_name":"MBrun","display_name":"M. Brün","profile_url":"https://iwu.academia.edu/MBrun?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_69900673 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="69900673"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 69900673, container: ".js-paper-rank-work_69900673", }); });</script></li><li class="js-percentile-work_69900673 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 69900673; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_69900673"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_69900673 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="69900673"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 69900673; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=69900673]").text(description); $(".js-view-count-work_69900673").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_69900673").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="69900673"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">12</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1681" rel="nofollow" href="https://www.academia.edu/Documents/in/Decision_Making">Decision Making</a>, <script data-card-contents-for-ri="1681" type="text/json">{"id":1681,"name":"Decision Making","url":"https://www.academia.edu/Documents/in/Decision_Making?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="12119" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_Engineering">Financial Engineering</a>, <script data-card-contents-for-ri="12119" type="text/json">{"id":12119,"name":"Financial Engineering","url":"https://www.academia.edu/Documents/in/Financial_Engineering?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="26327" rel="nofollow" href="https://www.academia.edu/Documents/in/Medicine">Medicine</a><script data-card-contents-for-ri="26327" type="text/json">{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=69900673]'), work: {"id":69900673,"title":"Financial Prediction and Trading Strategies Using","created_at":"2022-01-29T04:16:47.428-08:00","url":"https://www.academia.edu/69900673/Financial_Prediction_and_Trading_Strategies_Using?f_ri=61227","dom_id":"work_69900673","summary":"Neuro[uzzy approaches for predicting financial time series are investigated and shown to perfarm well in the context of various trading strategies. The horizon of prediction is typically a few days and trading strategies are examined using historical data. A methodology is presented where neurnl predictors are used to anticipate the general behavior of financial indices (moving up, down, or staying cOJlstant) in the context of stocks and options trading. The methodology is tested with actual financial data and shows considerable promise as a decision making and planning tool.","downloadable_attachments":[],"ordered_authors":[{"id":46375718,"first_name":"M.","last_name":"Brün","domain_name":"iwu","page_name":"MBrun","display_name":"M. Brün","profile_url":"https://iwu.academia.edu/MBrun?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=61227","nofollow":true},{"id":1681,"name":"Decision Making","url":"https://www.academia.edu/Documents/in/Decision_Making?f_ri=61227","nofollow":true},{"id":12119,"name":"Financial Engineering","url":"https://www.academia.edu/Documents/in/Financial_Engineering?f_ri=61227","nofollow":true},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine?f_ri=61227","nofollow":true},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary?f_ri=61227"},{"id":31900,"name":"Fuzzy","url":"https://www.academia.edu/Documents/in/Fuzzy?f_ri=61227"},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227"},{"id":221822,"name":"Historical Data","url":"https://www.academia.edu/Documents/in/Historical_Data?f_ri=61227"},{"id":310562,"name":"Fuzzy Neural Network","url":"https://www.academia.edu/Documents/in/Fuzzy_Neural_Network?f_ri=61227"},{"id":580777,"name":"Neural","url":"https://www.academia.edu/Documents/in/Neural?f_ri=61227"},{"id":749302,"name":"Indexation","url":"https://www.academia.edu/Documents/in/Indexation?f_ri=61227"},{"id":2036700,"name":"Trading Strategy","url":"https://www.academia.edu/Documents/in/Trading_Strategy?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_6934426" data-work_id="6934426" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/6934426/The_Use_of_Domain_Knowledge_in_Feature_Construction_for_Financial_Time_Series_Prediction">The Use of Domain Knowledge in Feature Construction for Financial Time Series Prediction</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Most of the existing data mining approaches to time series prediction use as training data an embed of the most recent values of the time series, following the traditional linear auto-regressive methodologies. However, in many time series... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_6934426" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Most of the existing data mining approaches to time series prediction use as training data an embed of the most recent values of the time series, following the traditional linear auto-regressive methodologies. However, in many time series prediction tasks the alternative approach that uses derivative features constructed from the raw data with the help of domain theories can produce significant prediction accuracy improvements. This is particularly noticeable when the available data includes multivariate information although the aim is still the prediction of one particular time series. This latter situation occurs frequently in financial time series prediction. This paper presents a method of feature construction based on domain knowledge that uses multivariate time series information. We show that this method improves the accuracy of next-day stock quotes prediction when compared with the traditional embed of historical values extracted from the original data.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/6934426" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c491347e59193bd7463b4e6961cbe175" rel="nofollow" data-download="{"attachment_id":48661715,"asset_id":6934426,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/48661715/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="11704088" href="https://up-pt.academia.edu/Lu%C3%ADsTorgo">Luís Torgo</a><script data-card-contents-for-user="11704088" type="text/json">{"id":11704088,"first_name":"Luís","last_name":"Torgo","domain_name":"up-pt","page_name":"LuísTorgo","display_name":"Luís Torgo","profile_url":"https://up-pt.academia.edu/Lu%C3%ADsTorgo?f_ri=61227","photo":"https://0.academia-photos.com/11704088/3375210/3971622/s65_lu_s.torgo.jpg"}</script></span></span></li><li class="js-paper-rank-work_6934426 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="6934426"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 6934426, container: ".js-paper-rank-work_6934426", }); });</script></li><li class="js-percentile-work_6934426 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 6934426; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_6934426"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_6934426 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="6934426"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 6934426; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=6934426]").text(description); $(".js-view-count-work_6934426").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_6934426").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="6934426"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">10</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="465" rel="nofollow" href="https://www.academia.edu/Documents/in/Artificial_Intelligence">Artificial Intelligence</a>, <script data-card-contents-for-ri="465" type="text/json">{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2009" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4205" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Analysis">Data Analysis</a>, <script data-card-contents-for-ri="4205" type="text/json">{"id":4205,"name":"Data Analysis","url":"https://www.academia.edu/Documents/in/Data_Analysis?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4456" rel="nofollow" href="https://www.academia.edu/Documents/in/Time_Series">Time Series</a><script data-card-contents-for-ri="4456" type="text/json">{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=6934426]'), work: {"id":6934426,"title":"The Use of Domain Knowledge in Feature Construction for Financial Time Series Prediction","created_at":"2014-05-01T18:13:42.208-07:00","url":"https://www.academia.edu/6934426/The_Use_of_Domain_Knowledge_in_Feature_Construction_for_Financial_Time_Series_Prediction?f_ri=61227","dom_id":"work_6934426","summary":"Most of the existing data mining approaches to time series prediction use as training data an embed of the most recent values of the time series, following the traditional linear auto-regressive methodologies. However, in many time series prediction tasks the alternative approach that uses derivative features constructed from the raw data with the help of domain theories can produce significant prediction accuracy improvements. This is particularly noticeable when the available data includes multivariate information although the aim is still the prediction of one particular time series. This latter situation occurs frequently in financial time series prediction. This paper presents a method of feature construction based on domain knowledge that uses multivariate time series information. We show that this method improves the accuracy of next-day stock quotes prediction when compared with the traditional embed of historical values extracted from the original data.","downloadable_attachments":[{"id":48661715,"asset_id":6934426,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":11704088,"first_name":"Luís","last_name":"Torgo","domain_name":"up-pt","page_name":"LuísTorgo","display_name":"Luís Torgo","profile_url":"https://up-pt.academia.edu/Lu%C3%ADsTorgo?f_ri=61227","photo":"https://0.academia-photos.com/11704088/3375210/3971622/s65_lu_s.torgo.jpg"}],"research_interests":[{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=61227","nofollow":true},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=61227","nofollow":true},{"id":4205,"name":"Data Analysis","url":"https://www.academia.edu/Documents/in/Data_Analysis?f_ri=61227","nofollow":true},{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true},{"id":11128,"name":"Information Extraction","url":"https://www.academia.edu/Documents/in/Information_Extraction?f_ri=61227"},{"id":46429,"name":"Economy","url":"https://www.academia.edu/Documents/in/Economy?f_ri=61227"},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227"},{"id":172418,"name":"Feature Construction","url":"https://www.academia.edu/Documents/in/Feature_Construction?f_ri=61227"},{"id":197861,"name":"Domain Knowledge","url":"https://www.academia.edu/Documents/in/Domain_Knowledge?f_ri=61227"},{"id":246163,"name":"Knowledge base","url":"https://www.academia.edu/Documents/in/Knowledge_base?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_16861238" data-work_id="16861238" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/16861238/Correlation_Based_Hierarchical_Clustering_in_Financial_Time_Series">Correlation Based Hierarchical Clustering in Financial Time Series</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We review a correlation based clustering procedure applied to a portfolio of assets synchronously traded in a financial market. The portfolio considered consists of the set of 500 highly capitalized stocks traded at the New York Stock... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_16861238" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We review a correlation based clustering procedure applied to a portfolio of assets synchronously traded in a financial market. The portfolio considered consists of the set of 500 highly capitalized stocks traded at the New York Stock Exchange during the time period 1987-1998. We show that meaningful economic information can be extracted from correlation matrices.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/16861238" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d0c8ebad9224e02a14be54b9a0d866ee" rel="nofollow" data-download="{"attachment_id":42381943,"asset_id":16861238,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/42381943/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="30673958" href="https://unipa.academia.edu/RosarioMantegna">Rosario N Mantegna</a><script data-card-contents-for-user="30673958" type="text/json">{"id":30673958,"first_name":"Rosario","last_name":"Mantegna","domain_name":"unipa","page_name":"RosarioMantegna","display_name":"Rosario N Mantegna","profile_url":"https://unipa.academia.edu/RosarioMantegna?f_ri=61227","photo":"https://0.academia-photos.com/30673958/11526629/160765055/s65_rosario.mantegna.jpg"}</script></span></span></li><li class="js-paper-rank-work_16861238 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="16861238"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 16861238, container: ".js-paper-rank-work_16861238", }); });</script></li><li class="js-percentile-work_16861238 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16861238; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_16861238"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_16861238 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="16861238"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16861238; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16861238]").text(description); $(".js-view-count-work_16861238").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_16861238").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="16861238"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="61227" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_time_series">Financial time series</a>, <script data-card-contents-for-ri="61227" type="text/json">{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="247780" rel="nofollow" href="https://www.academia.edu/Documents/in/Hierarchical_Clustering">Hierarchical Clustering</a>, <script data-card-contents-for-ri="247780" type="text/json">{"id":247780,"name":"Hierarchical Clustering","url":"https://www.academia.edu/Documents/in/Hierarchical_Clustering?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="270673" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_Market">Financial Market</a>, <script data-card-contents-for-ri="270673" type="text/json">{"id":270673,"name":"Financial Market","url":"https://www.academia.edu/Documents/in/Financial_Market?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="899955" rel="nofollow" href="https://www.academia.edu/Documents/in/New_York_Stock_Exchange">New York Stock Exchange</a><script data-card-contents-for-ri="899955" type="text/json">{"id":899955,"name":"New York Stock Exchange","url":"https://www.academia.edu/Documents/in/New_York_Stock_Exchange?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=16861238]'), work: {"id":16861238,"title":"Correlation Based Hierarchical Clustering in Financial Time Series","created_at":"2015-10-16T00:10:08.449-07:00","url":"https://www.academia.edu/16861238/Correlation_Based_Hierarchical_Clustering_in_Financial_Time_Series?f_ri=61227","dom_id":"work_16861238","summary":"We review a correlation based clustering procedure applied to a portfolio of assets synchronously traded in a financial market. The portfolio considered consists of the set of 500 highly capitalized stocks traded at the New York Stock Exchange during the time period 1987-1998. We show that meaningful economic information can be extracted from correlation matrices.","downloadable_attachments":[{"id":42381943,"asset_id":16861238,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":30673958,"first_name":"Rosario","last_name":"Mantegna","domain_name":"unipa","page_name":"RosarioMantegna","display_name":"Rosario N Mantegna","profile_url":"https://unipa.academia.edu/RosarioMantegna?f_ri=61227","photo":"https://0.academia-photos.com/30673958/11526629/160765055/s65_rosario.mantegna.jpg"}],"research_interests":[{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true},{"id":247780,"name":"Hierarchical Clustering","url":"https://www.academia.edu/Documents/in/Hierarchical_Clustering?f_ri=61227","nofollow":true},{"id":270673,"name":"Financial Market","url":"https://www.academia.edu/Documents/in/Financial_Market?f_ri=61227","nofollow":true},{"id":899955,"name":"New York Stock Exchange","url":"https://www.academia.edu/Documents/in/New_York_Stock_Exchange?f_ri=61227","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_1188196" data-work_id="1188196" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/1188196/Neural_network_model_selection_for_financial_time_series_prediction">Neural network model selection for financial time series prediction</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Can neural network model selection be guided by statistical procedures such as hypothesis tests, information criteria and cross-validation? Recently, Anders and Kom (1999) proposed five neural network model specification strategies based... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_1188196" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Can neural network model selection be guided by statistical procedures such as hypothesis tests, information criteria and cross-validation? Recently, Anders and Kom (1999) proposed five neural network model specification strategies based on different statistical procedures. In this paper, we use and adapt the Anders-Koru framework to find appropriate neural network models for financial time series prediction. The most important new issue in this context is the specification of IIII. dynamic structure of the models, i.e. the selection of the lagged values of the input time series. A linear model is built with full dynamic structure, then its possihl« nonlinear extensions are tested using a statistical procedure inspired by thl' Anders-Kom approach. Promising results are obtained with an application 10 predict the monthly time series of mortgage loans purchased in The Netherlands.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/1188196" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="3f956aeaf99a07cf28bed5bef4c162a2" rel="nofollow" data-download="{"attachment_id":7354682,"asset_id":1188196,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/7354682/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1081868" href="https://unicas.academia.edu/FrancescoVirili">Francesco Virili</a><script data-card-contents-for-user="1081868" type="text/json">{"id":1081868,"first_name":"Francesco","last_name":"Virili","domain_name":"unicas","page_name":"FrancescoVirili","display_name":"Francesco Virili","profile_url":"https://unicas.academia.edu/FrancescoVirili?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_1188196 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="1188196"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 1188196, container: ".js-paper-rank-work_1188196", }); 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$(".js-view-count[data-work-id=1188196]").text(description); $(".js-view-count-work_1188196").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_1188196").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="1188196"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="4388" rel="nofollow" href="https://www.academia.edu/Documents/in/Computational_Statistics">Computational Statistics</a>, <script data-card-contents-for-ri="4388" type="text/json">{"id":4388,"name":"Computational Statistics","url":"https://www.academia.edu/Documents/in/Computational_Statistics?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4456" rel="nofollow" href="https://www.academia.edu/Documents/in/Time_Series">Time Series</a>, <script data-card-contents-for-ri="4456" type="text/json">{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="61227" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_time_series">Financial time series</a>, <script data-card-contents-for-ri="61227" type="text/json">{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="80414" rel="nofollow" href="https://www.academia.edu/Documents/in/Mathematical_Sciences">Mathematical Sciences</a><script data-card-contents-for-ri="80414" type="text/json">{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=1188196]'), work: {"id":1188196,"title":"Neural network model selection for financial time series prediction","created_at":"2011-12-29T23:50:22.141-08:00","url":"https://www.academia.edu/1188196/Neural_network_model_selection_for_financial_time_series_prediction?f_ri=61227","dom_id":"work_1188196","summary":"Can neural network model selection be guided by statistical procedures such as hypothesis tests, information criteria and cross-validation? Recently, Anders and Kom (1999) proposed five neural network model specification strategies based on different statistical procedures. In this paper, we use and adapt the Anders-Koru framework to find appropriate neural network models for financial time series prediction. The most important new issue in this context is the specification of IIII. dynamic structure of the models, i.e. the selection of the lagged values of the input time series. A linear model is built with full dynamic structure, then its possihl« nonlinear extensions are tested using a statistical procedure inspired by thl' Anders-Kom approach. Promising results are obtained with an application 10 predict the monthly time series of mortgage loans purchased in The Netherlands.","downloadable_attachments":[{"id":7354682,"asset_id":1188196,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1081868,"first_name":"Francesco","last_name":"Virili","domain_name":"unicas","page_name":"FrancescoVirili","display_name":"Francesco Virili","profile_url":"https://unicas.academia.edu/FrancescoVirili?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":4388,"name":"Computational Statistics","url":"https://www.academia.edu/Documents/in/Computational_Statistics?f_ri=61227","nofollow":true},{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences?f_ri=61227","nofollow":true},{"id":1837730,"name":"Neural Network Model","url":"https://www.academia.edu/Documents/in/Neural_Network_Model?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_48594236" data-work_id="48594236" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/48594236/Support_vector_machine_with_adaptive_parameters_in_financial_time_series_forecasting">Support vector machine with adaptive parameters in financial time series forecasting</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_48594236" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. This paper deals with the application of SVM in financial time series forecasting. The feasibility of applying SVM in financial forecasting is first examined by comparing it with the multilayer back-propagation (BP) neural network and the regularized radial basis function (RBF) neural network. The variability in performance of SVM with respect to the free parameters is investigated experimentally. Adaptive parameters are then proposed by incorporating the nonstationarity of financial time series into SVM. Five real futures contracts collated from the Chicago Mercantile Market are used as the data sets. The simulation shows that among the three methods, SVM outperforms the BP neural network in financial forecasting, and there are comparable generalization performance between SVM and the regularized RBF neural network. Furthermore, the free parameters of SVM have a great effect on the generalization performance. SVM with adaptive parameters can both achieve higher generalization performance and use fewer support vectors than the standard SVM in financial forecasting.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/48594236" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="98ac3f5b421991366f1691721c990c04" rel="nofollow" data-download="{"attachment_id":67125288,"asset_id":48594236,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/67125288/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="154127646" href="https://nus.academia.edu/FrancisTay">Francis Tay</a><script data-card-contents-for-user="154127646" type="text/json">{"id":154127646,"first_name":"Francis","last_name":"Tay","domain_name":"nus","page_name":"FrancisTay","display_name":"Francis Tay","profile_url":"https://nus.academia.edu/FrancisTay?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_48594236 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="48594236"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 48594236, container: ".js-paper-rank-work_48594236", }); });</script></li><li class="js-percentile-work_48594236 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 48594236; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_48594236"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_48594236 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="48594236"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 48594236; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=48594236]").text(description); $(".js-view-count-work_48594236").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_48594236").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="48594236"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5750" rel="nofollow" href="https://www.academia.edu/Documents/in/Back_Propagation">Back Propagation</a>, <script data-card-contents-for-ri="5750" type="text/json">{"id":5750,"name":"Back Propagation","url":"https://www.academia.edu/Documents/in/Back_Propagation?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5751" rel="nofollow" href="https://www.academia.edu/Documents/in/Radial_Basis_Function">Radial Basis Function</a>, <script data-card-contents-for-ri="5751" type="text/json">{"id":5751,"name":"Radial Basis Function","url":"https://www.academia.edu/Documents/in/Radial_Basis_Function?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="26066" rel="nofollow" href="https://www.academia.edu/Documents/in/Neural_Network">Neural Network</a>, <script data-card-contents-for-ri="26066" type="text/json">{"id":26066,"name":"Neural Network","url":"https://www.academia.edu/Documents/in/Neural_Network?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="28235" rel="nofollow" href="https://www.academia.edu/Documents/in/Multidisciplinary">Multidisciplinary</a><script data-card-contents-for-ri="28235" type="text/json">{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=48594236]'), work: {"id":48594236,"title":"Support vector machine with adaptive parameters in financial time series forecasting","created_at":"2021-05-05T00:05:39.817-07:00","url":"https://www.academia.edu/48594236/Support_vector_machine_with_adaptive_parameters_in_financial_time_series_forecasting?f_ri=61227","dom_id":"work_48594236","summary":"A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. This paper deals with the application of SVM in financial time series forecasting. The feasibility of applying SVM in financial forecasting is first examined by comparing it with the multilayer back-propagation (BP) neural network and the regularized radial basis function (RBF) neural network. The variability in performance of SVM with respect to the free parameters is investigated experimentally. Adaptive parameters are then proposed by incorporating the nonstationarity of financial time series into SVM. Five real futures contracts collated from the Chicago Mercantile Market are used as the data sets. The simulation shows that among the three methods, SVM outperforms the BP neural network in financial forecasting, and there are comparable generalization performance between SVM and the regularized RBF neural network. Furthermore, the free parameters of SVM have a great effect on the generalization performance. SVM with adaptive parameters can both achieve higher generalization performance and use fewer support vectors than the standard SVM in financial forecasting.","downloadable_attachments":[{"id":67125288,"asset_id":48594236,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":154127646,"first_name":"Francis","last_name":"Tay","domain_name":"nus","page_name":"FrancisTay","display_name":"Francis Tay","profile_url":"https://nus.academia.edu/FrancisTay?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":5750,"name":"Back Propagation","url":"https://www.academia.edu/Documents/in/Back_Propagation?f_ri=61227","nofollow":true},{"id":5751,"name":"Radial Basis Function","url":"https://www.academia.edu/Documents/in/Radial_Basis_Function?f_ri=61227","nofollow":true},{"id":26066,"name":"Neural Network","url":"https://www.academia.edu/Documents/in/Neural_Network?f_ri=61227","nofollow":true},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227"},{"id":191289,"name":"Support vector machine","url":"https://www.academia.edu/Documents/in/Support_vector_machine?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_79087656" data-work_id="79087656" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/79087656/Statistical_analysis_of_financial_time_series_under_the_assumption_of_local_stationarity">Statistical analysis of financial time series under the assumption of local stationarity</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The aim of this paper is to apply a nonparametric methodology developped by Donoho, Mallat, von Sachs & Samuelides (2003) for estimating an autocovariance sequence to the statistical analysis of the return of securities and discuss the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_79087656" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The aim of this paper is to apply a nonparametric methodology developped by Donoho, Mallat, von Sachs & Samuelides (2003) for estimating an autocovariance sequence to the statistical analysis of the return of securities and discuss the advantages offered by this approach over other existing methods like fixed-window-length segmentation procedures. Theoretical properties of adaptivity of this estimation method have been proved for a specific class of time series, namely the class of locally stationary processes, with an autocovariance structure which varies slowly over time in most cases but might exhibit abrupt changes of regime. This method is based on an algorithm that selects empirically from the data the tiling of the time-frequency plane which exposes best in the least squares sense the underlying second-order time-varying structure of the time series, and so may properly describe the time-inhomogeneous variations of speculative prices. The applications we consider here mainly concern the analysis of structural changes occuring in stock market returns, VaR estimation and the comparison between the variation structure of stock indexes returns in developed markets and in developing markets</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/79087656" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="133b394a8eeef816ec4027c42cc93e61" rel="nofollow" data-download="{"attachment_id":85925905,"asset_id":79087656,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/85925905/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="32962974" href="https://telecom-paristech.academia.edu/St%C3%A9phanCl%C3%A9men%C3%A7on">Stéphan Clémençon</a><script data-card-contents-for-user="32962974" type="text/json">{"id":32962974,"first_name":"Stéphan","last_name":"Clémençon","domain_name":"telecom-paristech","page_name":"StéphanClémençon","display_name":"Stéphan Clémençon","profile_url":"https://telecom-paristech.academia.edu/St%C3%A9phanCl%C3%A9men%C3%A7on?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_79087656 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="79087656"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 79087656, container: ".js-paper-rank-work_79087656", }); });</script></li><li class="js-percentile-work_79087656 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79087656; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_79087656"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_79087656 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="79087656"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79087656; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79087656]").text(description); $(".js-view-count-work_79087656").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_79087656").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="79087656"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">16</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="724" rel="nofollow" href="https://www.academia.edu/Documents/in/Economics">Economics</a>, <script data-card-contents-for-ri="724" type="text/json">{"id":724,"name":"Economics","url":"https://www.academia.edu/Documents/in/Economics?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="747" rel="nofollow" href="https://www.academia.edu/Documents/in/Econometrics">Econometrics</a>, <script data-card-contents-for-ri="747" type="text/json">{"id":747,"name":"Econometrics","url":"https://www.academia.edu/Documents/in/Econometrics?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2141" rel="nofollow" href="https://www.academia.edu/Documents/in/Signal_Processing">Signal Processing</a>, <script data-card-contents-for-ri="2141" type="text/json">{"id":2141,"name":"Signal Processing","url":"https://www.academia.edu/Documents/in/Signal_Processing?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4456" rel="nofollow" href="https://www.academia.edu/Documents/in/Time_Series">Time Series</a><script data-card-contents-for-ri="4456" type="text/json">{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=79087656]'), work: {"id":79087656,"title":"Statistical analysis of financial time series under the assumption of local stationarity","created_at":"2022-05-13T15:20:24.659-07:00","url":"https://www.academia.edu/79087656/Statistical_analysis_of_financial_time_series_under_the_assumption_of_local_stationarity?f_ri=61227","dom_id":"work_79087656","summary":"The aim of this paper is to apply a nonparametric methodology developped by Donoho, Mallat, von Sachs \u0026 Samuelides (2003) for estimating an autocovariance sequence to the statistical analysis of the return of securities and discuss the advantages offered by this approach over other existing methods like fixed-window-length segmentation procedures. Theoretical properties of adaptivity of this estimation method have been proved for a specific class of time series, namely the class of locally stationary processes, with an autocovariance structure which varies slowly over time in most cases but might exhibit abrupt changes of regime. This method is based on an algorithm that selects empirically from the data the tiling of the time-frequency plane which exposes best in the least squares sense the underlying second-order time-varying structure of the time series, and so may properly describe the time-inhomogeneous variations of speculative prices. The applications we consider here mainly concern the analysis of structural changes occuring in stock market returns, VaR estimation and the comparison between the variation structure of stock indexes returns in developed markets and in developing markets","downloadable_attachments":[{"id":85925905,"asset_id":79087656,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":32962974,"first_name":"Stéphan","last_name":"Clémençon","domain_name":"telecom-paristech","page_name":"StéphanClémençon","display_name":"Stéphan Clémençon","profile_url":"https://telecom-paristech.academia.edu/St%C3%A9phanCl%C3%A9men%C3%A7on?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":724,"name":"Economics","url":"https://www.academia.edu/Documents/in/Economics?f_ri=61227","nofollow":true},{"id":747,"name":"Econometrics","url":"https://www.academia.edu/Documents/in/Econometrics?f_ri=61227","nofollow":true},{"id":2141,"name":"Signal Processing","url":"https://www.academia.edu/Documents/in/Signal_Processing?f_ri=61227","nofollow":true},{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true},{"id":5187,"name":"Statistical Analysis","url":"https://www.academia.edu/Documents/in/Statistical_Analysis?f_ri=61227"},{"id":42620,"name":"Time-Frequency Analysis","url":"https://www.academia.edu/Documents/in/Time-Frequency_Analysis?f_ri=61227"},{"id":48739,"name":"Quantitative Finance","url":"https://www.academia.edu/Documents/in/Quantitative_Finance?f_ri=61227"},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences?f_ri=61227"},{"id":205509,"name":"Time Frequency Analysis","url":"https://www.academia.edu/Documents/in/Time_Frequency_Analysis?f_ri=61227"},{"id":213801,"name":"Structural Change","url":"https://www.academia.edu/Documents/in/Structural_Change?f_ri=61227"},{"id":347272,"name":"Second Order","url":"https://www.academia.edu/Documents/in/Second_Order?f_ri=61227"},{"id":749302,"name":"Indexation","url":"https://www.academia.edu/Documents/in/Indexation?f_ri=61227"},{"id":1180343,"name":"Estimation Method","url":"https://www.academia.edu/Documents/in/Estimation_Method?f_ri=61227"},{"id":1480215,"name":"Time varying","url":"https://www.academia.edu/Documents/in/Time_varying?f_ri=61227"},{"id":2795801,"name":"Stock Market Returns","url":"https://www.academia.edu/Documents/in/Stock_Market_Returns?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_4669668" data-work_id="4669668" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/4669668/THRESHOLD_AUTOREGRESSIVE_MODELING_IN_FINANCE_THE_PRICE_DIFFERENCES_OF_EQUIVALENT_ASSETS">THRESHOLD AUTOREGRESSIVE MODELING IN FINANCE: THE PRICE DIFFERENCES OF EQUIVALENT ASSETS</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Threshold autoregressive (TAR) models condition the first moment of a time series on lagged information using a step-function-type nonlinear structure. TAR techniques are expected to be relevant in financial time-series modeling in... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_4669668" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Threshold autoregressive (TAR) models condition the first moment of a time series on lagged information using a step-function-type nonlinear structure. TAR techniques are expected to be relevant in financial time-series modeling in situations where deviations of prices from equilibrium values depend on discrete transaction costs and where market regulators follow intervention rules based on threshold values of control variables. an important finance application is in modeling the difference in prices of equivalent assets in the presence of transaction costs. the focus of this paper is on motivating the use of TAR models in this context and on the statistical estimation and testing procedures. the procedures are illustrated by modeling the difference between the prices of an index futures contract and the equivalent underlying cash index. It is found that the hypothesis of linearity is conclusively rejected in favor of threshold nonlinearity and that the estimated thresholds are largely consistent with arbitrage-related transaction costs.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/4669668" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="dcb1b527a137da0cb6544d5871788671" rel="nofollow" data-download="{"attachment_id":49705488,"asset_id":4669668,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/49705488/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="5926880" href="https://independent.academia.edu/PradeepYadav14">Pradeep Yadav</a><script data-card-contents-for-user="5926880" type="text/json">{"id":5926880,"first_name":"Pradeep","last_name":"Yadav","domain_name":"independent","page_name":"PradeepYadav14","display_name":"Pradeep Yadav","profile_url":"https://independent.academia.edu/PradeepYadav14?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_4669668 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="4669668"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 4669668, container: ".js-paper-rank-work_4669668", }); });</script></li><li class="js-percentile-work_4669668 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 4669668; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_4669668"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_4669668 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="4669668"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4669668; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=4669668]").text(description); $(".js-view-count-work_4669668").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_4669668").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="4669668"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">13</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="305" rel="nofollow" href="https://www.academia.edu/Documents/in/Applied_Mathematics">Applied Mathematics</a>, <script data-card-contents-for-ri="305" type="text/json">{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4456" rel="nofollow" href="https://www.academia.edu/Documents/in/Time_Series">Time Series</a>, <script data-card-contents-for-ri="4456" type="text/json">{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="24659" rel="nofollow" href="https://www.academia.edu/Documents/in/Mathematical_Finance">Mathematical Finance</a>, <script data-card-contents-for-ri="24659" type="text/json">{"id":24659,"name":"Mathematical Finance","url":"https://www.academia.edu/Documents/in/Mathematical_Finance?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="61227" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_time_series">Financial time series</a><script data-card-contents-for-ri="61227" type="text/json">{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=4669668]'), work: {"id":4669668,"title":"THRESHOLD AUTOREGRESSIVE MODELING IN FINANCE: THE PRICE DIFFERENCES OF EQUIVALENT ASSETS","created_at":"2013-10-03T18:40:47.978-07:00","url":"https://www.academia.edu/4669668/THRESHOLD_AUTOREGRESSIVE_MODELING_IN_FINANCE_THE_PRICE_DIFFERENCES_OF_EQUIVALENT_ASSETS?f_ri=61227","dom_id":"work_4669668","summary":"Threshold autoregressive (TAR) models condition the first moment of a time series on lagged information using a step-function-type nonlinear structure. TAR techniques are expected to be relevant in financial time-series modeling in situations where deviations of prices from equilibrium values depend on discrete transaction costs and where market regulators follow intervention rules based on threshold values of control variables. an important finance application is in modeling the difference in prices of equivalent assets in the presence of transaction costs. the focus of this paper is on motivating the use of TAR models in this context and on the statistical estimation and testing procedures. the procedures are illustrated by modeling the difference between the prices of an index futures contract and the equivalent underlying cash index. It is found that the hypothesis of linearity is conclusively rejected in favor of threshold nonlinearity and that the estimated thresholds are largely consistent with arbitrage-related transaction costs.","downloadable_attachments":[{"id":49705488,"asset_id":4669668,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":5926880,"first_name":"Pradeep","last_name":"Yadav","domain_name":"independent","page_name":"PradeepYadav14","display_name":"Pradeep Yadav","profile_url":"https://independent.academia.edu/PradeepYadav14?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics?f_ri=61227","nofollow":true},{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true},{"id":24659,"name":"Mathematical Finance","url":"https://www.academia.edu/Documents/in/Mathematical_Finance?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true},{"id":176461,"name":"Cointegration","url":"https://www.academia.edu/Documents/in/Cointegration?f_ri=61227"},{"id":467081,"name":"Functional Type","url":"https://www.academia.edu/Documents/in/Functional_Type?f_ri=61227"},{"id":511647,"name":"Threshold Autoregressive Model","url":"https://www.academia.edu/Documents/in/Threshold_Autoregressive_Model?f_ri=61227"},{"id":749302,"name":"Indexation","url":"https://www.academia.edu/Documents/in/Indexation?f_ri=61227"},{"id":764552,"name":"Arbitrage","url":"https://www.academia.edu/Documents/in/Arbitrage?f_ri=61227"},{"id":868794,"name":"Transaction Cost","url":"https://www.academia.edu/Documents/in/Transaction_Cost?f_ri=61227"},{"id":970225,"name":"Autoregressive","url":"https://www.academia.edu/Documents/in/Autoregressive?f_ri=61227"},{"id":1135703,"name":"Statistical Estimation","url":"https://www.academia.edu/Documents/in/Statistical_Estimation?f_ri=61227"},{"id":1216932,"name":"Rule Based","url":"https://www.academia.edu/Documents/in/Rule_Based?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_27359070" data-work_id="27359070" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/27359070/Stationarity_tests_for_financial_time_series">Stationarity tests for financial time series</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Much attention has been paid in recent years to the study of the order of integration of a time series, i.e. the number of di erences that are necessary to transform it into a stationary series. The relevance of the subject arises because... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_27359070" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Much attention has been paid in recent years to the study of the order of integration of a time series, i.e. the number of di erences that are necessary to transform it into a stationary series. The relevance of the subject arises because most of time series analysis in economics and ÿnance are based on the stationarity hypothesis. In the paper we present the most common tests for the null hypothesis of stationarity, and apply them to study the order of integration in a Spanish ÿnancial series namely the IBEX-35, using unit root tests as well. We ÿnd empirical evidence of a unit root in the series.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/27359070" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f669fe326f0a80a878c29bedde604867" rel="nofollow" data-download="{"attachment_id":47615014,"asset_id":27359070,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/47615014/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="51477557" href="https://uclm.academia.edu/RomanMinguez">Roman Minguez</a><script data-card-contents-for-user="51477557" type="text/json">{"id":51477557,"first_name":"Roman","last_name":"Minguez","domain_name":"uclm","page_name":"RomanMinguez","display_name":"Roman Minguez","profile_url":"https://uclm.academia.edu/RomanMinguez?f_ri=61227","photo":"https://0.academia-photos.com/51477557/13637745/14783681/s65_roman.minguez.jpg"}</script></span></span></li><li class="js-paper-rank-work_27359070 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="27359070"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 27359070, container: ".js-paper-rank-work_27359070", }); 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The relevance of the subject arises because most of time series analysis in economics and ÿnance are based on the stationarity hypothesis. In the paper we present the most common tests for the null hypothesis of stationarity, and apply them to study the order of integration in a Spanish ÿnancial series namely the IBEX-35, using unit root tests as well. We ÿnd empirical evidence of a unit root in the series.","downloadable_attachments":[{"id":47615014,"asset_id":27359070,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":51477557,"first_name":"Roman","last_name":"Minguez","domain_name":"uclm","page_name":"RomanMinguez","display_name":"Roman Minguez","profile_url":"https://uclm.academia.edu/RomanMinguez?f_ri=61227","photo":"https://0.academia-photos.com/51477557/13637745/14783681/s65_roman.minguez.jpg"}],"research_interests":[{"id":318,"name":"Mathematical Physics","url":"https://www.academia.edu/Documents/in/Mathematical_Physics?f_ri=61227","nofollow":true},{"id":518,"name":"Quantum Physics","url":"https://www.academia.edu/Documents/in/Quantum_Physics?f_ri=61227","nofollow":true},{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true},{"id":27708,"name":"Econophysics","url":"https://www.academia.edu/Documents/in/Econophysics?f_ri=61227","nofollow":true},{"id":30485,"name":"Time series analysis","url":"https://www.academia.edu/Documents/in/Time_series_analysis?f_ri=61227"},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227"},{"id":393134,"name":"Empirical evidence","url":"https://www.academia.edu/Documents/in/Empirical_evidence?f_ri=61227"},{"id":511649,"name":"Unit Root","url":"https://www.academia.edu/Documents/in/Unit_Root?f_ri=61227"},{"id":541021,"name":"Unit Root Test","url":"https://www.academia.edu/Documents/in/Unit_Root_Test?f_ri=61227"},{"id":1636539,"name":"Stationarity test","url":"https://www.academia.edu/Documents/in/Stationarity_test?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_356141" data-work_id="356141" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/356141/Stock_Market_Forecasting_Using_Hidden_Markov_Model_a_New_Approach">Stock Market Forecasting Using Hidden Markov Model: a New Approach</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/356141" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="8d54812d6500f0ce9153f276f24d2a5d" rel="nofollow" data-download="{"attachment_id":1904664,"asset_id":356141,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/1904664/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="273652" href="https://annauniv.academia.edu/Mdhassan">Md hassan</a><script data-card-contents-for-user="273652" type="text/json">{"id":273652,"first_name":"Md","last_name":"hassan","domain_name":"annauniv","page_name":"Mdhassan","display_name":"Md hassan","profile_url":"https://annauniv.academia.edu/Mdhassan?f_ri=61227","photo":"https://0.academia-photos.com/273652/56716/11064935/s65_md.hassan.jpg"}</script></span></span></li><li class="js-paper-rank-work_356141 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="356141"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 356141, container: ".js-paper-rank-work_356141", }); 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$(".js-view-count[data-work-id=356141]").text(description); $(".js-view-count-work_356141").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_356141").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="356141"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5109" rel="nofollow" href="https://www.academia.edu/Documents/in/Pattern_Recognition">Pattern Recognition</a>, <script data-card-contents-for-ri="5109" type="text/json">{"id":5109,"name":"Pattern Recognition","url":"https://www.academia.edu/Documents/in/Pattern_Recognition?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="29156" rel="nofollow" href="https://www.academia.edu/Documents/in/Stock_Market">Stock Market</a>, <script data-card-contents-for-ri="29156" type="text/json">{"id":29156,"name":"Stock Market","url":"https://www.academia.edu/Documents/in/Stock_Market?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="43619" rel="nofollow" href="https://www.academia.edu/Documents/in/Feature_Selection">Feature Selection</a>, <script data-card-contents-for-ri="43619" type="text/json">{"id":43619,"name":"Feature Selection","url":"https://www.academia.edu/Documents/in/Feature_Selection?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="61227" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_time_series">Financial time series</a><script data-card-contents-for-ri="61227" type="text/json">{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=356141]'), work: {"id":356141,"title":"Stock Market Forecasting Using Hidden Markov Model: a New Approach","created_at":"2010-10-29T13:11:41.534-07:00","url":"https://www.academia.edu/356141/Stock_Market_Forecasting_Using_Hidden_Markov_Model_a_New_Approach?f_ri=61227","dom_id":"work_356141","summary":null,"downloadable_attachments":[{"id":1904664,"asset_id":356141,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":273652,"first_name":"Md","last_name":"hassan","domain_name":"annauniv","page_name":"Mdhassan","display_name":"Md hassan","profile_url":"https://annauniv.academia.edu/Mdhassan?f_ri=61227","photo":"https://0.academia-photos.com/273652/56716/11064935/s65_md.hassan.jpg"}],"research_interests":[{"id":5109,"name":"Pattern Recognition","url":"https://www.academia.edu/Documents/in/Pattern_Recognition?f_ri=61227","nofollow":true},{"id":29156,"name":"Stock Market","url":"https://www.academia.edu/Documents/in/Stock_Market?f_ri=61227","nofollow":true},{"id":43619,"name":"Feature Selection","url":"https://www.academia.edu/Documents/in/Feature_Selection?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true},{"id":68937,"name":"Hidden Markov Models","url":"https://www.academia.edu/Documents/in/Hidden_Markov_Models?f_ri=61227"},{"id":143539,"name":"hidden Markov model","url":"https://www.academia.edu/Documents/in/hidden_Markov_model?f_ri=61227"},{"id":489225,"name":"Stock Price","url":"https://www.academia.edu/Documents/in/Stock_Price?f_ri=61227"},{"id":868912,"name":"Dynamic System","url":"https://www.academia.edu/Documents/in/Dynamic_System?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_15185531" data-work_id="15185531" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/15185531/Some_Recent_Developments_in_Futures_Hedging">Some Recent Developments in Futures Hedging</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The use of futures contracts as a hedging instrument has been the focus of much research. At the theoretical level, an optimal hedge strategy is traditionally based on the expected-utility maximization paradigm. A simplification of this... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_15185531" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The use of futures contracts as a hedging instrument has been the focus of much research. At the theoretical level, an optimal hedge strategy is traditionally based on the expected-utility maximization paradigm. A simplification of this paradigm leads to the minimum-variance criterion. Although this paradigm is quite well accepted, alternative approaches have been sought. At the empirical level, research on futures hedging has benefited from the recent developments in the econometrics literature. Much research has been done on improving the estimation of the optimal hedge ratio. As more is known about the statistical properties of financial time series, more sophisticated estimation methods are proposed. In this survey we review some recent developments in futures hedging. We delineate the theoretical underpinning of various methods and discuss the econometric implementation of the methods. . Of course, we are solely responsible for any omissions and commissions.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/15185531" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="37420add461bd6ea0c2bfe4aaf781835" rel="nofollow" data-download="{"attachment_id":43487152,"asset_id":15185531,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43487152/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="34244721" href="https://utsa.academia.edu/DonaldLien">Donald Lien</a><script data-card-contents-for-user="34244721" type="text/json">{"id":34244721,"first_name":"Donald","last_name":"Lien","domain_name":"utsa","page_name":"DonaldLien","display_name":"Donald Lien","profile_url":"https://utsa.academia.edu/DonaldLien?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_15185531 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="15185531"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 15185531, container: ".js-paper-rank-work_15185531", }); 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At the theoretical level, an optimal hedge strategy is traditionally based on the expected-utility maximization paradigm. A simplification of this paradigm leads to the minimum-variance criterion. Although this paradigm is quite well accepted, alternative approaches have been sought. At the empirical level, research on futures hedging has benefited from the recent developments in the econometrics literature. Much research has been done on improving the estimation of the optimal hedge ratio. As more is known about the statistical properties of financial time series, more sophisticated estimation methods are proposed. In this survey we review some recent developments in futures hedging. We delineate the theoretical underpinning of various methods and discuss the econometric implementation of the methods. . Of course, we are solely responsible for any omissions and commissions.","downloadable_attachments":[{"id":43487152,"asset_id":15185531,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":34244721,"first_name":"Donald","last_name":"Lien","domain_name":"utsa","page_name":"DonaldLien","display_name":"Donald Lien","profile_url":"https://utsa.academia.edu/DonaldLien?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":747,"name":"Econometrics","url":"https://www.academia.edu/Documents/in/Econometrics?f_ri=61227","nofollow":true},{"id":27659,"name":"Applied Economics","url":"https://www.academia.edu/Documents/in/Applied_Economics?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true},{"id":168068,"name":"Minimum variance","url":"https://www.academia.edu/Documents/in/Minimum_variance?f_ri=61227","nofollow":true},{"id":428833,"name":"Statistical Properties","url":"https://www.academia.edu/Documents/in/Statistical_Properties?f_ri=61227"},{"id":1180343,"name":"Estimation Method","url":"https://www.academia.edu/Documents/in/Estimation_Method?f_ri=61227"},{"id":1894115,"name":"Hedge Ratio","url":"https://www.academia.edu/Documents/in/Hedge_Ratio?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_15162103" data-work_id="15162103" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/15162103/A_semiparametric_Bayesian_approach_to_the_analysis_of_financial_time_series_with_applications_to_value_at_risk_estimation">A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Financial time series analysis deals with the understanding of data collected on financial markets. Several parametric distribution models have been entertained for describing, estimating and predicting the dynamics of financial time... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_15162103" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Financial time series analysis deals with the understanding of data collected on financial markets. Several parametric distribution models have been entertained for describing, estimating and predicting the dynamics of financial time series. Alternatively, this article considers a Bayesian semiparametric approach. In particular, the usual parametric distributional assumptions of the GARCH-type models are relaxed by entertaining the class of location-scale mixtures</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/15162103" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0544e9640b253a7a2508158ae29f2e95" rel="nofollow" data-download="{"attachment_id":38563563,"asset_id":15162103,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/38563563/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="34209553" href="https://independent.academia.edu/GaleanoPedro">Pedro Galeano</a><script data-card-contents-for-user="34209553" type="text/json">{"id":34209553,"first_name":"Pedro","last_name":"Galeano","domain_name":"independent","page_name":"GaleanoPedro","display_name":"Pedro Galeano","profile_url":"https://independent.academia.edu/GaleanoPedro?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_15162103 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="15162103"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 15162103, container: ".js-paper-rank-work_15162103", }); 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$(".js-view-count[data-work-id=15162103]").text(description); $(".js-view-count-work_15162103").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_15162103").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="15162103"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">17</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="28235" rel="nofollow" href="https://www.academia.edu/Documents/in/Multidisciplinary">Multidisciplinary</a>, <script data-card-contents-for-ri="28235" type="text/json">{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="34109" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_estimation">Bayesian estimation</a>, <script data-card-contents-for-ri="34109" type="text/json">{"id":34109,"name":"Bayesian estimation","url":"https://www.academia.edu/Documents/in/Bayesian_estimation?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="61227" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_time_series">Financial time series</a>, <script data-card-contents-for-ri="61227" type="text/json">{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="67968" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_Inference">Statistical Inference</a><script data-card-contents-for-ri="67968" type="text/json">{"id":67968,"name":"Statistical Inference","url":"https://www.academia.edu/Documents/in/Statistical_Inference?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=15162103]'), work: {"id":15162103,"title":"A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation","created_at":"2015-08-25T01:16:58.295-07:00","url":"https://www.academia.edu/15162103/A_semiparametric_Bayesian_approach_to_the_analysis_of_financial_time_series_with_applications_to_value_at_risk_estimation?f_ri=61227","dom_id":"work_15162103","summary":"Financial time series analysis deals with the understanding of data collected on financial markets. Several parametric distribution models have been entertained for describing, estimating and predicting the dynamics of financial time series. Alternatively, this article considers a Bayesian semiparametric approach. In particular, the usual parametric distributional assumptions of the GARCH-type models are relaxed by entertaining the class of location-scale mixtures","downloadable_attachments":[{"id":38563563,"asset_id":15162103,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":34209553,"first_name":"Pedro","last_name":"Galeano","domain_name":"independent","page_name":"GaleanoPedro","display_name":"Pedro Galeano","profile_url":"https://independent.academia.edu/GaleanoPedro?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary?f_ri=61227","nofollow":true},{"id":34109,"name":"Bayesian estimation","url":"https://www.academia.edu/Documents/in/Bayesian_estimation?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true},{"id":67968,"name":"Statistical Inference","url":"https://www.academia.edu/Documents/in/Statistical_Inference?f_ri=61227","nofollow":true},{"id":85262,"name":"Markov Chain Monte Carlo","url":"https://www.academia.edu/Documents/in/Markov_Chain_Monte_Carlo?f_ri=61227"},{"id":94431,"name":"Value at Risk","url":"https://www.academia.edu/Documents/in/Value_at_Risk?f_ri=61227"},{"id":153168,"name":"Data Collection","url":"https://www.academia.edu/Documents/in/Data_Collection?f_ri=61227"},{"id":219931,"name":"Mixture of Gaussians","url":"https://www.academia.edu/Documents/in/Mixture_of_Gaussians?f_ri=61227"},{"id":270673,"name":"Financial Market","url":"https://www.academia.edu/Documents/in/Financial_Market?f_ri=61227"},{"id":311931,"name":"STOCK EXCHANGE","url":"https://www.academia.edu/Documents/in/STOCK_EXCHANGE?f_ri=61227"},{"id":373540,"name":"Bayesian model","url":"https://www.academia.edu/Documents/in/Bayesian_model?f_ri=61227"},{"id":509785,"name":"Simulation Study","url":"https://www.academia.edu/Documents/in/Simulation_Study?f_ri=61227"},{"id":512859,"name":"Gaussian Mixture","url":"https://www.academia.edu/Documents/in/Gaussian_Mixture?f_ri=61227"},{"id":749302,"name":"Indexation","url":"https://www.academia.edu/Documents/in/Indexation?f_ri=61227"},{"id":991622,"name":"Market Risk","url":"https://www.academia.edu/Documents/in/Market_Risk?f_ri=61227"},{"id":1142720,"name":"Normal Distribution","url":"https://www.academia.edu/Documents/in/Normal_Distribution?f_ri=61227"},{"id":1951089,"name":"Bayesian Estimator","url":"https://www.academia.edu/Documents/in/Bayesian_Estimator?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_5418429" data-work_id="5418429" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/5418429/Agent_based_models_of_financial_markets">Agent-based models of financial markets</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This review deals with several microscopic ("agent-based") models of financial markets which have been studied by economists and physicists over the last decade: Kim-Markowitz, Levy-Levy-Solomon, Cont-Bouchaud, Solomon-Weisbuch,... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_5418429" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This review deals with several microscopic ("agent-based") models of financial markets which have been studied by economists and physicists over the last decade: Kim-Markowitz, Levy-Levy-Solomon, Cont-Bouchaud, Solomon-Weisbuch, Lux-Marchesi, Donangelo-Sneppen and Solomon-Levy-Huang. After an overview of simulation approaches in financial economics, we first give a summary of the Donangelo-Sneppen model of monetary exchange and compare it with related models in economics literature. Our selective review then outlines the main ingredients of some influential early models of multi-agent dynamics in financial markets (Kim-Markowitz, Levy-Levy-Solomon). As will be seen, these contributions draw their inspiration from the complex appearance of investors' interactions in real-life markets. Their main aim is to reproduce (and, thereby, provide possible explanations) for the spectacular bubbles and crashes seen in certain historical episodes, but they lack (like almost all the work before 1998 or so) a perspective in terms of the universal statistical features of financial time series. In fact, awareness of a set of such regularities (power-law tails of the distribution of returns, temporal scaling of volatility) only gradually appeared over the nineties. With the more precise description of the formerly relatively vague characteristics ( e.g. moving from the notion of fat tails to the more concrete one of a power-law with index around three), it became clear that financial markets dynamics give rise to some kind of universal scaling laws. Showing similarities with scaling laws for other systems with many interacting sub-units, an exploration of financial markets as multi-agent systems appeared to be a natural consequence. This topic was pursued by quite a number of contributions appearing in both the physics and economics literature since the late nineties. From the wealth of different flavors of multi-agent models that have appeared by now, we discuss the Cont-Bouchaud, Solomon-Levy-Huang and Lux-Marchesi models. Open research questions are discussed in our concluding section.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/5418429" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="50f658d01ae01223b6cdaabb6d8cc8b0" rel="nofollow" data-download="{"attachment_id":39078506,"asset_id":5418429,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/39078506/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="7571818" href="https://ifw-kiel.academia.edu/ThomasLux">Thomas Lux</a><script data-card-contents-for-user="7571818" type="text/json">{"id":7571818,"first_name":"Thomas","last_name":"Lux","domain_name":"ifw-kiel","page_name":"ThomasLux","display_name":"Thomas Lux","profile_url":"https://ifw-kiel.academia.edu/ThomasLux?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_5418429 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="5418429"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 5418429, container: ".js-paper-rank-work_5418429", }); });</script></li><li class="js-percentile-work_5418429 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 5418429; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_5418429"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_5418429 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="5418429"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 5418429; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=5418429]").text(description); $(".js-view-count-work_5418429").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_5418429").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="5418429"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">12</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="748" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_Economics">Financial Economics</a>, <script data-card-contents-for-ri="748" type="text/json">{"id":748,"name":"Financial Economics","url":"https://www.academia.edu/Documents/in/Financial_Economics?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="45873" rel="nofollow" href="https://www.academia.edu/Documents/in/Multi_Agent_System">Multi Agent System</a>, <script data-card-contents-for-ri="45873" type="text/json">{"id":45873,"name":"Multi Agent System","url":"https://www.academia.edu/Documents/in/Multi_Agent_System?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="61227" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_time_series">Financial time series</a>, <script data-card-contents-for-ri="61227" type="text/json">{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="80414" rel="nofollow" href="https://www.academia.edu/Documents/in/Mathematical_Sciences">Mathematical Sciences</a><script data-card-contents-for-ri="80414" type="text/json">{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=5418429]'), work: {"id":5418429,"title":"Agent-based models of financial markets","created_at":"2013-12-13T17:10:59.035-08:00","url":"https://www.academia.edu/5418429/Agent_based_models_of_financial_markets?f_ri=61227","dom_id":"work_5418429","summary":"This review deals with several microscopic (\"agent-based\") models of financial markets which have been studied by economists and physicists over the last decade: Kim-Markowitz, Levy-Levy-Solomon, Cont-Bouchaud, Solomon-Weisbuch, Lux-Marchesi, Donangelo-Sneppen and Solomon-Levy-Huang. After an overview of simulation approaches in financial economics, we first give a summary of the Donangelo-Sneppen model of monetary exchange and compare it with related models in economics literature. Our selective review then outlines the main ingredients of some influential early models of multi-agent dynamics in financial markets (Kim-Markowitz, Levy-Levy-Solomon). As will be seen, these contributions draw their inspiration from the complex appearance of investors' interactions in real-life markets. Their main aim is to reproduce (and, thereby, provide possible explanations) for the spectacular bubbles and crashes seen in certain historical episodes, but they lack (like almost all the work before 1998 or so) a perspective in terms of the universal statistical features of financial time series. In fact, awareness of a set of such regularities (power-law tails of the distribution of returns, temporal scaling of volatility) only gradually appeared over the nineties. With the more precise description of the formerly relatively vague characteristics ( e.g. moving from the notion of fat tails to the more concrete one of a power-law with index around three), it became clear that financial markets dynamics give rise to some kind of universal scaling laws. Showing similarities with scaling laws for other systems with many interacting sub-units, an exploration of financial markets as multi-agent systems appeared to be a natural consequence. This topic was pursued by quite a number of contributions appearing in both the physics and economics literature since the late nineties. From the wealth of different flavors of multi-agent models that have appeared by now, we discuss the Cont-Bouchaud, Solomon-Levy-Huang and Lux-Marchesi models. Open research questions are discussed in our concluding section.","downloadable_attachments":[{"id":39078506,"asset_id":5418429,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":7571818,"first_name":"Thomas","last_name":"Lux","domain_name":"ifw-kiel","page_name":"ThomasLux","display_name":"Thomas Lux","profile_url":"https://ifw-kiel.academia.edu/ThomasLux?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":748,"name":"Financial Economics","url":"https://www.academia.edu/Documents/in/Financial_Economics?f_ri=61227","nofollow":true},{"id":45873,"name":"Multi Agent System","url":"https://www.academia.edu/Documents/in/Multi_Agent_System?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences?f_ri=61227","nofollow":true},{"id":113890,"name":"Power Law","url":"https://www.academia.edu/Documents/in/Power_Law?f_ri=61227"},{"id":118582,"name":"Physical sciences","url":"https://www.academia.edu/Documents/in/Physical_sciences?f_ri=61227"},{"id":173004,"name":"Relational Model","url":"https://www.academia.edu/Documents/in/Relational_Model?f_ri=61227"},{"id":249843,"name":"Agent Modeling","url":"https://www.academia.edu/Documents/in/Agent_Modeling?f_ri=61227"},{"id":270673,"name":"Financial Market","url":"https://www.academia.edu/Documents/in/Financial_Market?f_ri=61227"},{"id":741144,"name":"Agent Based Model","url":"https://www.academia.edu/Documents/in/Agent_Based_Model?f_ri=61227"},{"id":749302,"name":"Indexation","url":"https://www.academia.edu/Documents/in/Indexation?f_ri=61227"},{"id":778976,"name":"Scaling Law","url":"https://www.academia.edu/Documents/in/Scaling_Law?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_18319526" data-work_id="18319526" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/18319526/The_k_factor_Gegenbauer_asymmetric_Power_GARCH_approach_for_modelling_electricity_spot_price_dynamics">The k-factor Gegenbauer asymmetric Power GARCH approach for modelling electricity spot price dynamics</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Electricity spot prices exhibit a number of typical features that are not found in most financial time series, such as complex seasonality patterns, persistence (hyperbolic decay of the autocorrelation function), mean reversion, spikes,... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_18319526" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Electricity spot prices exhibit a number of typical features that are not found in most financial time series, such as complex seasonality patterns, persistence (hyperbolic decay of the autocorrelation function), mean reversion, spikes, asymmetric behavior and leptokurtosis. Efforts have been made worldwide to model the behaviour of the electricity&#39;s market price. In this paper, we propose a new approach dealing</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/18319526" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="829e985b170860607e84cc2f3ec1b3a9" rel="nofollow" data-download="{"attachment_id":39992615,"asset_id":18319526,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/39992615/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="38309892" href="https://independent.academia.edu/AbdouDiongue">Abdou Diongue</a><script data-card-contents-for-user="38309892" type="text/json">{"id":38309892,"first_name":"Abdou","last_name":"Diongue","domain_name":"independent","page_name":"AbdouDiongue","display_name":"Abdou Diongue","profile_url":"https://independent.academia.edu/AbdouDiongue?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_18319526 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="18319526"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 18319526, container: ".js-paper-rank-work_18319526", }); });</script></li><li class="js-percentile-work_18319526 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 18319526; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_18319526"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_18319526 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="18319526"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 18319526; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=18319526]").text(description); $(".js-view-count-work_18319526").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_18319526").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="18319526"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="57433" rel="nofollow" href="https://www.academia.edu/Documents/in/Seasonality">Seasonality</a>, <script data-card-contents-for-ri="57433" type="text/json">{"id":57433,"name":"Seasonality","url":"https://www.academia.edu/Documents/in/Seasonality?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="61227" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_time_series">Financial time series</a>, <script data-card-contents-for-ri="61227" type="text/json">{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="98134" rel="nofollow" href="https://www.academia.edu/Documents/in/United_States">United States</a>, <script data-card-contents-for-ri="98134" type="text/json">{"id":98134,"name":"United States","url":"https://www.academia.edu/Documents/in/United_States?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="142697" rel="nofollow" href="https://www.academia.edu/Documents/in/New_Jersey">New Jersey</a><script data-card-contents-for-ri="142697" type="text/json">{"id":142697,"name":"New Jersey","url":"https://www.academia.edu/Documents/in/New_Jersey?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=18319526]'), work: {"id":18319526,"title":"The k-factor Gegenbauer asymmetric Power GARCH approach for modelling electricity spot price dynamics","created_at":"2015-11-14T03:56:26.316-08:00","url":"https://www.academia.edu/18319526/The_k_factor_Gegenbauer_asymmetric_Power_GARCH_approach_for_modelling_electricity_spot_price_dynamics?f_ri=61227","dom_id":"work_18319526","summary":"Electricity spot prices exhibit a number of typical features that are not found in most financial time series, such as complex seasonality patterns, persistence (hyperbolic decay of the autocorrelation function), mean reversion, spikes, asymmetric behavior and leptokurtosis. Efforts have been made worldwide to model the behaviour of the electricity\u0026#39;s market price. In this paper, we propose a new approach dealing","downloadable_attachments":[{"id":39992615,"asset_id":18319526,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":38309892,"first_name":"Abdou","last_name":"Diongue","domain_name":"independent","page_name":"AbdouDiongue","display_name":"Abdou Diongue","profile_url":"https://independent.academia.edu/AbdouDiongue?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":57433,"name":"Seasonality","url":"https://www.academia.edu/Documents/in/Seasonality?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true},{"id":98134,"name":"United States","url":"https://www.academia.edu/Documents/in/United_States?f_ri=61227","nofollow":true},{"id":142697,"name":"New Jersey","url":"https://www.academia.edu/Documents/in/New_Jersey?f_ri=61227","nofollow":true},{"id":1118571,"name":"Autocorrelation Function","url":"https://www.academia.edu/Documents/in/Autocorrelation_Function?f_ri=61227"},{"id":1709206,"name":"Mean Reversion","url":"https://www.academia.edu/Documents/in/Mean_Reversion?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_29157854" data-work_id="29157854" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/29157854/Fractional_diffusion_in_finance_Basic_theory">Fractional diffusion in finance: Basic theory</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this paper we present a rather general phenomenological theory of tick-bytick dynamics in financial markets, based on the continuous time random walk (CTRW) model. The theory can take into account the possibility of the non-Markovian... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_29157854" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this paper we present a rather general phenomenological theory of tick-bytick dynamics in financial markets, based on the continuous time random walk (CTRW) model. The theory can take into account the possibility of the non-Markovian character of financial time series by means of a generalized master equation with a time fractional derivative. We present predictions on the behaviour of the waiting-time probability density whose decay interpolates from a stretched exponential at small times to a power-law for long times. A proper transition to the so-called diffusion or hydrodynamic limit is also discussed by using scaling arguments. It turns out that the probability density function obeys a generalized diffusion equation of fractional order both in space and in time. Finally, a general representation of the fundamental solution of the fractional diffusion equation is given, which leads to a general scaling property for the the probability density function, henceforth to a statistical self-similarity for the limiting process.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/29157854" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="e1f4d597ab72ef9168972f3caab1b5da" rel="nofollow" data-download="{"attachment_id":49602509,"asset_id":29157854,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/49602509/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="55024788" href="https://independent.academia.edu/RudolfGorenflo">Rudolf Gorenflo</a><script data-card-contents-for-user="55024788" type="text/json">{"id":55024788,"first_name":"Rudolf","last_name":"Gorenflo","domain_name":"independent","page_name":"RudolfGorenflo","display_name":"Rudolf Gorenflo","profile_url":"https://independent.academia.edu/RudolfGorenflo?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_29157854 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="29157854"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 29157854, container: ".js-paper-rank-work_29157854", }); });</script></li><li class="js-percentile-work_29157854 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 29157854; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_29157854"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_29157854 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="29157854"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 29157854; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=29157854]").text(description); $(".js-view-count-work_29157854").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_29157854").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="29157854"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">9</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="61227" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_time_series">Financial time series</a>, <script data-card-contents-for-ri="61227" type="text/json">{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="113890" rel="nofollow" href="https://www.academia.edu/Documents/in/Power_Law">Power Law</a>, <script data-card-contents-for-ri="113890" type="text/json">{"id":113890,"name":"Power Law","url":"https://www.academia.edu/Documents/in/Power_Law?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="270673" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_Market">Financial Market</a>, <script data-card-contents-for-ri="270673" type="text/json">{"id":270673,"name":"Financial Market","url":"https://www.academia.edu/Documents/in/Financial_Market?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="364894" rel="nofollow" href="https://www.academia.edu/Documents/in/Fundamental_Solution">Fundamental Solution</a><script data-card-contents-for-ri="364894" type="text/json">{"id":364894,"name":"Fundamental Solution","url":"https://www.academia.edu/Documents/in/Fundamental_Solution?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=29157854]'), work: {"id":29157854,"title":"Fractional diffusion in finance: Basic theory","created_at":"2016-10-14T11:14:45.355-07:00","url":"https://www.academia.edu/29157854/Fractional_diffusion_in_finance_Basic_theory?f_ri=61227","dom_id":"work_29157854","summary":"In this paper we present a rather general phenomenological theory of tick-bytick dynamics in financial markets, based on the continuous time random walk (CTRW) model. The theory can take into account the possibility of the non-Markovian character of financial time series by means of a generalized master equation with a time fractional derivative. We present predictions on the behaviour of the waiting-time probability density whose decay interpolates from a stretched exponential at small times to a power-law for long times. A proper transition to the so-called diffusion or hydrodynamic limit is also discussed by using scaling arguments. It turns out that the probability density function obeys a generalized diffusion equation of fractional order both in space and in time. Finally, a general representation of the fundamental solution of the fractional diffusion equation is given, which leads to a general scaling property for the the probability density function, henceforth to a statistical self-similarity for the limiting process.","downloadable_attachments":[{"id":49602509,"asset_id":29157854,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":55024788,"first_name":"Rudolf","last_name":"Gorenflo","domain_name":"independent","page_name":"RudolfGorenflo","display_name":"Rudolf Gorenflo","profile_url":"https://independent.academia.edu/RudolfGorenflo?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true},{"id":113890,"name":"Power Law","url":"https://www.academia.edu/Documents/in/Power_Law?f_ri=61227","nofollow":true},{"id":270673,"name":"Financial Market","url":"https://www.academia.edu/Documents/in/Financial_Market?f_ri=61227","nofollow":true},{"id":364894,"name":"Fundamental Solution","url":"https://www.academia.edu/Documents/in/Fundamental_Solution?f_ri=61227","nofollow":true},{"id":444561,"name":"Acoustic Diffusion Equation Model","url":"https://www.academia.edu/Documents/in/Acoustic_Diffusion_Equation_Model?f_ri=61227"},{"id":588227,"name":"PROBABILITY DENSITY","url":"https://www.academia.edu/Documents/in/PROBABILITY_DENSITY?f_ri=61227"},{"id":872399,"name":"Probability Density Function","url":"https://www.academia.edu/Documents/in/Probability_Density_Function?f_ri=61227"},{"id":1158716,"name":"Waiting Time","url":"https://www.academia.edu/Documents/in/Waiting_Time?f_ri=61227"},{"id":2537611,"name":"Fractional Derivative","url":"https://www.academia.edu/Documents/in/Fractional_Derivative?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_19177623" data-work_id="19177623" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/19177623/%D0%86%D0%BD%D1%84%D0%BE%D1%80%D0%BC%D0%B0%D1%86%D1%96%D0%B9%D0%BD%D0%B0_%D1%81%D0%B8%D1%81%D1%82%D0%B5%D0%BC%D0%B0_%D0%B4%D0%BB%D1%8F_%D0%BF%D1%80%D0%BE%D0%B3%D0%BD%D0%BE%D0%B7%D1%83%D0%B2%D0%B0%D0%BD%D0%BD%D1%8F_%D1%96_%D0%BF%D1%80%D0%B8%D0%B9%D0%BD%D1%8F%D1%82%D1%82%D1%8F_%D1%80%D1%96%D1%88%D0%B5%D0%BD%D1%8C_%D1%83_%D1%84%D1%96%D0%BD%D0%B0%D0%BD%D1%81%D0%BE%D0%B2%D1%96%D0%B9_%D1%81%D1%84%D0%B5%D1%80%D1%96">Інформаційна система для прогнозування і прийняття рішень у фінансовій сфері</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Побудовано автоматизовану персональну локальну інформаційну систему обробки даних як систему прогнозування і підтримки прийняття рішень у фінансовому секторі, в якій синтезовані запропоновані математичні моделі та методи прогнозування... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_19177623" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Побудовано автоматизовану персональну локальну інформаційну систему обробки даних як<br />систему прогнозування і підтримки прийняття рішень у фінансовому секторі, в якій синтезовані<br />запропоновані математичні моделі та методи прогнозування фінансових часових рядів.<br />Інформаційна система забезпечує виконання таких задач: передпрогнозний фрактальний аналіз<br />часового ряду, реалізація комбінованих моделей прогнозування рівнів та знаків приростів часових<br />рядів, ідентифікація моментів зміни їх тенденцій для прийняття фінансових рішень.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item 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Белощицкий","profile_url":"https://knuba.academia.edu/%D0%90%D0%BD%D0%B4%D1%80%D0%B5%D0%B9%D0%90%D0%BB%D0%B5%D0%BA%D1%81%D0%B0%D0%BD%D0%B4%D1%80%D0%BE%D0%B2%D0%B8%D1%87%D0%91%D0%B5%D0%BB%D0%BE%D1%89%D0%B8%D1%86%D0%BA%D0%B8%D0%B9?f_ri=61227","photo":"https://0.academia-photos.com/36341511/10456254/11667162/s65__._.jpg"}</script></span></span></li><li class="js-paper-rank-work_19177623 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="19177623"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 19177623, container: ".js-paper-rank-work_19177623", }); });</script></li><li class="js-percentile-work_19177623 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x 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сфері","created_at":"2015-11-29T07:03:47.630-08:00","url":"https://www.academia.edu/19177623/%D0%86%D0%BD%D1%84%D0%BE%D1%80%D0%BC%D0%B0%D1%86%D1%96%D0%B9%D0%BD%D0%B0_%D1%81%D0%B8%D1%81%D1%82%D0%B5%D0%BC%D0%B0_%D0%B4%D0%BB%D1%8F_%D0%BF%D1%80%D0%BE%D0%B3%D0%BD%D0%BE%D0%B7%D1%83%D0%B2%D0%B0%D0%BD%D0%BD%D1%8F_%D1%96_%D0%BF%D1%80%D0%B8%D0%B9%D0%BD%D1%8F%D1%82%D1%82%D1%8F_%D1%80%D1%96%D1%88%D0%B5%D0%BD%D1%8C_%D1%83_%D1%84%D1%96%D0%BD%D0%B0%D0%BD%D1%81%D0%BE%D0%B2%D1%96%D0%B9_%D1%81%D1%84%D0%B5%D1%80%D1%96?f_ri=61227","dom_id":"work_19177623","summary":"Побудовано автоматизовану персональну локальну інформаційну систему обробки даних як\nсистему прогнозування і підтримки прийняття рішень у фінансовому секторі, в якій синтезовані\nзапропоновані математичні моделі та методи прогнозування фінансових часових рядів.\nІнформаційна система забезпечує виконання таких задач: передпрогнозний фрактальний аналіз\nчасового ряду, реалізація комбінованих моделей прогнозування рівнів та знаків приростів часових\nрядів, ідентифікація моментів зміни їх тенденцій для прийняття фінансових рішень.","downloadable_attachments":[{"id":40474028,"asset_id":19177623,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":36341511,"first_name":"Андрей Александрович","last_name":"Белощицкий","domain_name":"knuba","page_name":"АндрейАлександровичБелощицкий","display_name":"Андрей Александрович Белощицкий","profile_url":"https://knuba.academia.edu/%D0%90%D0%BD%D0%B4%D1%80%D0%B5%D0%B9%D0%90%D0%BB%D0%B5%D0%BA%D1%81%D0%B0%D0%BD%D0%B4%D1%80%D0%BE%D0%B2%D0%B8%D1%87%D0%91%D0%B5%D0%BB%D0%BE%D1%89%D0%B8%D1%86%D0%BA%D0%B8%D0%B9?f_ri=61227","photo":"https://0.academia-photos.com/36341511/10456254/11667162/s65__._.jpg"}],"research_interests":[{"id":1681,"name":"Decision Making","url":"https://www.academia.edu/Documents/in/Decision_Making?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time 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the increasing use of time series data has initiated various research and development attempts in the field of data and knowledge management. Time series data is characterized as large in data size, high dimensionality and... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_14680076" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Recently, the increasing use of time series data has initiated various research and development attempts in the field of data and knowledge management. Time series data is characterized as large in data size, high dimensionality and update continuously. Moreover, the time series data is always considered as a whole instead of individual numerical fields. Indeed, a large set of time series data is from stock market. Stock time series has its own characteristics over other time series. Moreover, dimensionality reduction is an essential step before many time series analysis and mining tasks. For these reasons, research is prompted to augment existing technologies and build new representation to manage financial time series data. In this paper, financial time series is represented according to the importance of the data points. With the concept of data point importance, a tree data structure, which supports incremental updating, is proposed to represent the time series and an access method for retrieving the time series data point from the tree, which is according to their order of importance, is introduced. This technique is capable to present the time series in different levels of detail and facilitate multi-resolution dimensionality reduction of the time series data. In this paper, different data point importance evaluation methods, a new updating method and two dimensionality reduction approaches are proposed and evaluated by a series of experiments. Finally, the application of the proposed representation on mobile environment is demonstrated. r .hk (T.-c. Fu).</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/14680076" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="11d42ccb1e359a89f1312a1d0337a03e" rel="nofollow" data-download="{"attachment_id":43982433,"asset_id":14680076,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43982433/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="33627837" href="https://polyu.academia.edu/RobertLuk">Robert Luk</a><script data-card-contents-for-user="33627837" type="text/json">{"id":33627837,"first_name":"Robert","last_name":"Luk","domain_name":"polyu","page_name":"RobertLuk","display_name":"Robert Luk","profile_url":"https://polyu.academia.edu/RobertLuk?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_14680076 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="14680076"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 14680076, container: ".js-paper-rank-work_14680076", }); 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$(".js-view-count[data-work-id=14680076]").text(description); $(".js-view-count-work_14680076").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_14680076").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="14680076"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">13</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="48" rel="nofollow" href="https://www.academia.edu/Documents/in/Engineering">Engineering</a>, <script data-card-contents-for-ri="48" type="text/json">{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1241" rel="nofollow" href="https://www.academia.edu/Documents/in/Knowledge_Management">Knowledge Management</a>, <script data-card-contents-for-ri="1241" type="text/json">{"id":1241,"name":"Knowledge Management","url":"https://www.academia.edu/Documents/in/Knowledge_Management?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4456" rel="nofollow" href="https://www.academia.edu/Documents/in/Time_Series">Time Series</a>, <script data-card-contents-for-ri="4456" type="text/json">{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="29156" rel="nofollow" href="https://www.academia.edu/Documents/in/Stock_Market">Stock Market</a><script data-card-contents-for-ri="29156" type="text/json">{"id":29156,"name":"Stock Market","url":"https://www.academia.edu/Documents/in/Stock_Market?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=14680076]'), work: {"id":14680076,"title":"Representing financial time series based on data point importance","created_at":"2015-08-05T00:48:52.093-07:00","url":"https://www.academia.edu/14680076/Representing_financial_time_series_based_on_data_point_importance?f_ri=61227","dom_id":"work_14680076","summary":"Recently, the increasing use of time series data has initiated various research and development attempts in the field of data and knowledge management. Time series data is characterized as large in data size, high dimensionality and update continuously. Moreover, the time series data is always considered as a whole instead of individual numerical fields. Indeed, a large set of time series data is from stock market. Stock time series has its own characteristics over other time series. Moreover, dimensionality reduction is an essential step before many time series analysis and mining tasks. For these reasons, research is prompted to augment existing technologies and build new representation to manage financial time series data. In this paper, financial time series is represented according to the importance of the data points. With the concept of data point importance, a tree data structure, which supports incremental updating, is proposed to represent the time series and an access method for retrieving the time series data point from the tree, which is according to their order of importance, is introduced. This technique is capable to present the time series in different levels of detail and facilitate multi-resolution dimensionality reduction of the time series data. In this paper, different data point importance evaluation methods, a new updating method and two dimensionality reduction approaches are proposed and evaluated by a series of experiments. Finally, the application of the proposed representation on mobile environment is demonstrated. r .hk (T.-c. Fu).","downloadable_attachments":[{"id":43982433,"asset_id":14680076,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":33627837,"first_name":"Robert","last_name":"Luk","domain_name":"polyu","page_name":"RobertLuk","display_name":"Robert Luk","profile_url":"https://polyu.academia.edu/RobertLuk?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=61227","nofollow":true},{"id":1241,"name":"Knowledge Management","url":"https://www.academia.edu/Documents/in/Knowledge_Management?f_ri=61227","nofollow":true},{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true},{"id":29156,"name":"Stock Market","url":"https://www.academia.edu/Documents/in/Stock_Market?f_ri=61227","nofollow":true},{"id":30485,"name":"Time series analysis","url":"https://www.academia.edu/Documents/in/Time_series_analysis?f_ri=61227"},{"id":30718,"name":"Level Of Detail (LOD)","url":"https://www.academia.edu/Documents/in/Level_Of_Detail_LOD_?f_ri=61227"},{"id":53994,"name":"Data Structure","url":"https://www.academia.edu/Documents/in/Data_Structure?f_ri=61227"},{"id":55018,"name":"Mobile Application","url":"https://www.academia.edu/Documents/in/Mobile_Application?f_ri=61227"},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227"},{"id":129891,"name":"Research and Development","url":"https://www.academia.edu/Documents/in/Research_and_Development-2?f_ri=61227"},{"id":181785,"name":"Time Series Data","url":"https://www.academia.edu/Documents/in/Time_Series_Data?f_ri=61227"},{"id":557801,"name":"High Dimensionality","url":"https://www.academia.edu/Documents/in/High_Dimensionality?f_ri=61227"},{"id":993289,"name":"Multi Resolution Transform","url":"https://www.academia.edu/Documents/in/Multi_Resolution_Transform?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_36495298" data-work_id="36495298" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/36495298/AN_EMPIRICAL_COMPARISON_OF_THE_ACADEMIC_PERFORMANCE_OF_STUDENTS_IN_THE_DISTANCE_LEARNING_AND_TRADITIONAL_CLASSROOM_ENVIRONMENT">AN EMPIRICAL COMPARISON OF THE ACADEMIC PERFORMANCE OF STUDENTS IN THE DISTANCE LEARNING AND TRADITIONAL CLASSROOM ENVIRONMENT</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The importance of distance learning programmes in tertiary institutions around the globe, cannot be overemphasized , as it provides an alternative mode of obtaining a university degree, through the use of information technology.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_36495298" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The importance of distance learning programmes in tertiary institutions around the globe, cannot be overemphasized , as it provides an alternative mode of obtaining a university degree, through the use of information technology. Currently, the world is operating in a technology and social media dominated era where millions of citizens can access limitless information. This study investigates the academic performance of graduates from traditional, and distance learning, modes of education in accounting and business administration courses, with the goal of determining the existence of differences in academic performance. The measure of academic performance considered in this study is the graduating Cumulative Grade Point Average (CGPA) of students. The results suggest that although marginal differences exist between the categories of students in performance, these differences are not significant enough to suggest difference in a performance due to the study mode, thus, this study concludes that performance of students in the selected courses is similar irrespective of the mode of education.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/36495298" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="797d429df017359743f64a09d9d8b1a9" rel="nofollow" data-download="{"attachment_id":56415017,"asset_id":36495298,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/56415017/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="3522766" href="https://run.academia.edu/DrOLUMIDEADESINA">Dr. OLUMIDE S ADESINA</a><script data-card-contents-for-user="3522766" type="text/json">{"id":3522766,"first_name":"Dr. OLUMIDE","last_name":"ADESINA","domain_name":"run","page_name":"DrOLUMIDEADESINA","display_name":"Dr. OLUMIDE S ADESINA","profile_url":"https://run.academia.edu/DrOLUMIDEADESINA?f_ri=61227","photo":"https://0.academia-photos.com/3522766/6189170/24702925/s65_dr._olumide.adesina.jpg"}</script></span></span></li><li class="js-paper-rank-work_36495298 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="36495298"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 36495298, container: ".js-paper-rank-work_36495298", }); });</script></li><li class="js-percentile-work_36495298 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 36495298; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_36495298"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_36495298 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="36495298"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 36495298; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=36495298]").text(description); $(".js-view-count-work_36495298").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_36495298").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="36495298"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="51529" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Inference">Bayesian Inference</a>, <script data-card-contents-for-ri="51529" type="text/json">{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="61227" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_time_series">Financial time series</a><script data-card-contents-for-ri="61227" type="text/json">{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=36495298]'), work: {"id":36495298,"title":"AN EMPIRICAL COMPARISON OF THE ACADEMIC PERFORMANCE OF STUDENTS IN THE DISTANCE LEARNING AND TRADITIONAL CLASSROOM ENVIRONMENT","created_at":"2018-04-25T03:07:40.545-07:00","url":"https://www.academia.edu/36495298/AN_EMPIRICAL_COMPARISON_OF_THE_ACADEMIC_PERFORMANCE_OF_STUDENTS_IN_THE_DISTANCE_LEARNING_AND_TRADITIONAL_CLASSROOM_ENVIRONMENT?f_ri=61227","dom_id":"work_36495298","summary":"The importance of distance learning programmes in tertiary institutions around the globe, cannot be overemphasized , as it provides an alternative mode of obtaining a university degree, through the use of information technology. Currently, the world is operating in a technology and social media dominated era where millions of citizens can access limitless information. This study investigates the academic performance of graduates from traditional, and distance learning, modes of education in accounting and business administration courses, with the goal of determining the existence of differences in academic performance. The measure of academic performance considered in this study is the graduating Cumulative Grade Point Average (CGPA) of students. The results suggest that although marginal differences exist between the categories of students in performance, these differences are not significant enough to suggest difference in a performance due to the study mode, thus, this study concludes that performance of students in the selected courses is similar irrespective of the mode of education.","downloadable_attachments":[{"id":56415017,"asset_id":36495298,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":3522766,"first_name":"Dr. OLUMIDE","last_name":"ADESINA","domain_name":"run","page_name":"DrOLUMIDEADESINA","display_name":"Dr. OLUMIDE S ADESINA","profile_url":"https://run.academia.edu/DrOLUMIDEADESINA?f_ri=61227","photo":"https://0.academia-photos.com/3522766/6189170/24702925/s65_dr._olumide.adesina.jpg"}],"research_interests":[{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_12404840" data-work_id="12404840" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/12404840/Econophysics_complex_correlations_and_trend_switchings_in_financial_time_series">Econophysics — complex correlations and trend switchings in financial time series</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This article focuses on the analysis of financial time series and their correlations. A method is used for quantifying pattern based correlations of a time series. With this methodology, evidence is found that typical behavioral patterns... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_12404840" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This article focuses on the analysis of financial time series and their correlations. A method is used for quantifying pattern based correlations of a time series. With this methodology, evidence is found that typical behavioral patterns of financial market participants manifest over short time scales, i.e., that reactions to given price patterns are not entirely random, but that similar price patterns also cause similar reactions. Based on the investigation of the complex correlations in financial time series, the question arises, which properties change when switching from a positive trend to a negative trend. An empirical quantification by rescaling provides the result that new price extrema coincide with a significant increase in transaction volume and a significant decrease in the length of corresponding time intervals between transactions. These findings are independent of the time scale over 9 orders of magnitude, and they exhibit characteristics which one can also find in other complex systems in nature (and in physical systems in particular). These properties are independent of the markets analyzed. Trends that exist only for a few seconds show the same characteristics as trends on time scales of several months. Thus, it is possible to study financial bubbles and their collapses in more detail, because trend switching processes occur with higher frequency on small time scales. In addition, a Monte Carlo based simulation of financial markets is analyzed and extended in order to reproduce empirical features and to gain insight into their causes. These causes include both financial market microstructure and the risk aversion of market participants.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/12404840" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="da969d6939ac631170be01be55bab5c7" rel="nofollow" data-download="{"attachment_id":46205420,"asset_id":12404840,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/46205420/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="31170267" href="https://warwick.academia.edu/TobiasPreis">Tobias Preis</a><script data-card-contents-for-user="31170267" type="text/json">{"id":31170267,"first_name":"Tobias","last_name":"Preis","domain_name":"warwick","page_name":"TobiasPreis","display_name":"Tobias Preis","profile_url":"https://warwick.academia.edu/TobiasPreis?f_ri=61227","photo":"https://0.academia-photos.com/31170267/9157329/10212093/s65_tobias.preis.jpg"}</script></span></span></li><li class="js-paper-rank-work_12404840 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="12404840"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 12404840, container: ".js-paper-rank-work_12404840", }); 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$(".js-view-count[data-work-id=12404840]").text(description); $(".js-view-count-work_12404840").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_12404840").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="12404840"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">17</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="2161" rel="nofollow" href="https://www.academia.edu/Documents/in/Microstructure">Microstructure</a>, <script data-card-contents-for-ri="2161" type="text/json">{"id":2161,"name":"Microstructure","url":"https://www.academia.edu/Documents/in/Microstructure?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4456" rel="nofollow" href="https://www.academia.edu/Documents/in/Time_Series">Time Series</a>, <script data-card-contents-for-ri="4456" type="text/json">{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="6177" rel="nofollow" href="https://www.academia.edu/Documents/in/Modeling">Modeling</a>, <script data-card-contents-for-ri="6177" type="text/json">{"id":6177,"name":"Modeling","url":"https://www.academia.edu/Documents/in/Modeling?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="6974" rel="nofollow" href="https://www.academia.edu/Documents/in/Monte_Carlo">Monte Carlo</a><script data-card-contents-for-ri="6974" type="text/json">{"id":6974,"name":"Monte Carlo","url":"https://www.academia.edu/Documents/in/Monte_Carlo?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=12404840]'), work: {"id":12404840,"title":"Econophysics — complex correlations and trend switchings in financial time series","created_at":"2015-05-15T16:02:38.724-07:00","url":"https://www.academia.edu/12404840/Econophysics_complex_correlations_and_trend_switchings_in_financial_time_series?f_ri=61227","dom_id":"work_12404840","summary":"This article focuses on the analysis of financial time series and their correlations. A method is used for quantifying pattern based correlations of a time series. With this methodology, evidence is found that typical behavioral patterns of financial market participants manifest over short time scales, i.e., that reactions to given price patterns are not entirely random, but that similar price patterns also cause similar reactions. Based on the investigation of the complex correlations in financial time series, the question arises, which properties change when switching from a positive trend to a negative trend. An empirical quantification by rescaling provides the result that new price extrema coincide with a significant increase in transaction volume and a significant decrease in the length of corresponding time intervals between transactions. These findings are independent of the time scale over 9 orders of magnitude, and they exhibit characteristics which one can also find in other complex systems in nature (and in physical systems in particular). These properties are independent of the markets analyzed. Trends that exist only for a few seconds show the same characteristics as trends on time scales of several months. Thus, it is possible to study financial bubbles and their collapses in more detail, because trend switching processes occur with higher frequency on small time scales. In addition, a Monte Carlo based simulation of financial markets is analyzed and extended in order to reproduce empirical features and to gain insight into their causes. These causes include both financial market microstructure and the risk aversion of market participants.","downloadable_attachments":[{"id":46205420,"asset_id":12404840,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":31170267,"first_name":"Tobias","last_name":"Preis","domain_name":"warwick","page_name":"TobiasPreis","display_name":"Tobias Preis","profile_url":"https://warwick.academia.edu/TobiasPreis?f_ri=61227","photo":"https://0.academia-photos.com/31170267/9157329/10212093/s65_tobias.preis.jpg"}],"research_interests":[{"id":2161,"name":"Microstructure","url":"https://www.academia.edu/Documents/in/Microstructure?f_ri=61227","nofollow":true},{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true},{"id":6177,"name":"Modeling","url":"https://www.academia.edu/Documents/in/Modeling?f_ri=61227","nofollow":true},{"id":6974,"name":"Monte Carlo","url":"https://www.academia.edu/Documents/in/Monte_Carlo?f_ri=61227","nofollow":true},{"id":13242,"name":"Market Structure","url":"https://www.academia.edu/Documents/in/Market_Structure?f_ri=61227"},{"id":54501,"name":"Complex System","url":"https://www.academia.edu/Documents/in/Complex_System?f_ri=61227"},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227"},{"id":69827,"name":"Risk Aversion","url":"https://www.academia.edu/Documents/in/Risk_Aversion?f_ri=61227"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences?f_ri=61227"},{"id":118582,"name":"Physical sciences","url":"https://www.academia.edu/Documents/in/Physical_sciences?f_ri=61227"},{"id":270673,"name":"Financial Market","url":"https://www.academia.edu/Documents/in/Financial_Market?f_ri=61227"},{"id":372874,"name":"Transaction Processing","url":"https://www.academia.edu/Documents/in/Transaction_Processing?f_ri=61227"},{"id":405713,"name":"Collapse","url":"https://www.academia.edu/Documents/in/Collapse?f_ri=61227"},{"id":473797,"name":"Microstructures","url":"https://www.academia.edu/Documents/in/Microstructures?f_ri=61227"},{"id":733224,"name":"Empirical Method","url":"https://www.academia.edu/Documents/in/Empirical_Method?f_ri=61227"},{"id":1333436,"name":"Monte Carlo Method","url":"https://www.academia.edu/Documents/in/Monte_Carlo_Method?f_ri=61227"},{"id":1475619,"name":"Switching Time","url":"https://www.academia.edu/Documents/in/Switching_Time?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_18580621" data-work_id="18580621" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/18580621/Econophysics_Empirical_facts_and_agent_based_models">Econophysics: Empirical facts and agent-based models</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This article aim at reviewing recent empirical and theoretical developments usually grouped under the term Econophysics. Since its name was coined in 1995 by merging the words "Economics" and "Physics", this new interdisciplinary field... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_18580621" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This article aim at reviewing recent empirical and theoretical developments usually grouped under the term Econophysics. Since its name was coined in 1995 by merging the words "Economics" and "Physics", this new interdisciplinary field has grown in various directions: theoretical macroeconomics (wealth distributions), microstructure of financial markets (order book modelling), econometrics of financial bubbles and crashes, etc. In the first part of the review, we begin with discussions on the interactions between Physics, Mathematics, Economics and Finance that led to the emergence of Econophysics. Then we present empirical studies revealing statistical properties of financial time series. We begin the presentation with the widely acknowledged "stylized facts" which describe the returns of financial assets -fat tails, volatility clustering, autocorrelation, etc. -and recall that some of these properties are directly linked to the way "time" is taken into account. We continue with the statistical properties observed on order books in financial markets. For the sake of illustrating this review, (nearly) all the stated facts are reproduced using our own high-frequency financial database. Finally, contributions to the study of correlations of assets such as random matrix theory and graph theory are presented. In the second part of the review, we deal with models in Econophysics through the point of view of agent-based modelling. Amongst a large number of multi-agent-based models, we have identified three representative areas. First, using previous work originally presented in the fields of behavioural finance and market microstructure theory, econophysicists have developed agent-based models of order-driven markets that are extensively presented here. Second, kinetic theory models designed to explain some empirical facts on wealth distribution are reviewed. Third, we briefly summarize game theory models by reviewing the now classic minority game and related problems.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/18580621" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="074c48b01d35bc46781ca56fd6be4624" rel="nofollow" data-download="{"attachment_id":40140005,"asset_id":18580621,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/40140005/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="38619200" href="https://independent.academia.edu/Fr%C3%A9d%C3%A9ricAbergel">Frédéric Abergel</a><script data-card-contents-for-user="38619200" type="text/json">{"id":38619200,"first_name":"Frédéric","last_name":"Abergel","domain_name":"independent","page_name":"FrédéricAbergel","display_name":"Frédéric Abergel","profile_url":"https://independent.academia.edu/Fr%C3%A9d%C3%A9ricAbergel?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_18580621 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="18580621"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 18580621, container: ".js-paper-rank-work_18580621", }); 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$(".js-view-count[data-work-id=18580621]").text(description); $(".js-view-count-work_18580621").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_18580621").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="18580621"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">16</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="757" rel="nofollow" href="https://www.academia.edu/Documents/in/Game_Theory">Game Theory</a>, <script data-card-contents-for-ri="757" type="text/json">{"id":757,"name":"Game Theory","url":"https://www.academia.edu/Documents/in/Game_Theory?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2616" rel="nofollow" href="https://www.academia.edu/Documents/in/Graph_Theory">Graph Theory</a>, <script data-card-contents-for-ri="2616" type="text/json">{"id":2616,"name":"Graph Theory","url":"https://www.academia.edu/Documents/in/Graph_Theory?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="7293" rel="nofollow" href="https://www.academia.edu/Documents/in/Market_Microstructure">Market Microstructure</a>, <script data-card-contents-for-ri="7293" type="text/json">{"id":7293,"name":"Market Microstructure","url":"https://www.academia.edu/Documents/in/Market_Microstructure?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="25221" rel="nofollow" href="https://www.academia.edu/Documents/in/Wealth_Distribution">Wealth Distribution</a><script data-card-contents-for-ri="25221" type="text/json">{"id":25221,"name":"Wealth Distribution","url":"https://www.academia.edu/Documents/in/Wealth_Distribution?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=18580621]'), work: {"id":18580621,"title":"Econophysics: Empirical facts and agent-based models","created_at":"2015-11-18T06:11:09.299-08:00","url":"https://www.academia.edu/18580621/Econophysics_Empirical_facts_and_agent_based_models?f_ri=61227","dom_id":"work_18580621","summary":"This article aim at reviewing recent empirical and theoretical developments usually grouped under the term Econophysics. Since its name was coined in 1995 by merging the words \"Economics\" and \"Physics\", this new interdisciplinary field has grown in various directions: theoretical macroeconomics (wealth distributions), microstructure of financial markets (order book modelling), econometrics of financial bubbles and crashes, etc. In the first part of the review, we begin with discussions on the interactions between Physics, Mathematics, Economics and Finance that led to the emergence of Econophysics. Then we present empirical studies revealing statistical properties of financial time series. We begin the presentation with the widely acknowledged \"stylized facts\" which describe the returns of financial assets -fat tails, volatility clustering, autocorrelation, etc. -and recall that some of these properties are directly linked to the way \"time\" is taken into account. We continue with the statistical properties observed on order books in financial markets. For the sake of illustrating this review, (nearly) all the stated facts are reproduced using our own high-frequency financial database. Finally, contributions to the study of correlations of assets such as random matrix theory and graph theory are presented. In the second part of the review, we deal with models in Econophysics through the point of view of agent-based modelling. Amongst a large number of multi-agent-based models, we have identified three representative areas. First, using previous work originally presented in the fields of behavioural finance and market microstructure theory, econophysicists have developed agent-based models of order-driven markets that are extensively presented here. Second, kinetic theory models designed to explain some empirical facts on wealth distribution are reviewed. Third, we briefly summarize game theory models by reviewing the now classic minority game and related problems.","downloadable_attachments":[{"id":40140005,"asset_id":18580621,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":38619200,"first_name":"Frédéric","last_name":"Abergel","domain_name":"independent","page_name":"FrédéricAbergel","display_name":"Frédéric Abergel","profile_url":"https://independent.academia.edu/Fr%C3%A9d%C3%A9ricAbergel?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":757,"name":"Game Theory","url":"https://www.academia.edu/Documents/in/Game_Theory?f_ri=61227","nofollow":true},{"id":2616,"name":"Graph Theory","url":"https://www.academia.edu/Documents/in/Graph_Theory?f_ri=61227","nofollow":true},{"id":7293,"name":"Market Microstructure","url":"https://www.academia.edu/Documents/in/Market_Microstructure?f_ri=61227","nofollow":true},{"id":25221,"name":"Wealth Distribution","url":"https://www.academia.edu/Documents/in/Wealth_Distribution?f_ri=61227","nofollow":true},{"id":48458,"name":"High Frequency","url":"https://www.academia.edu/Documents/in/High_Frequency?f_ri=61227"},{"id":56761,"name":"Agent Based Modelling","url":"https://www.academia.edu/Documents/in/Agent_Based_Modelling?f_ri=61227"},{"id":59051,"name":"Kinetic Theory","url":"https://www.academia.edu/Documents/in/Kinetic_Theory?f_ri=61227"},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227"},{"id":137277,"name":"Random Matrix Theory","url":"https://www.academia.edu/Documents/in/Random_Matrix_Theory?f_ri=61227"},{"id":219474,"name":"Empirical Study","url":"https://www.academia.edu/Documents/in/Empirical_Study?f_ri=61227"},{"id":270673,"name":"Financial Market","url":"https://www.academia.edu/Documents/in/Financial_Market?f_ri=61227"},{"id":428833,"name":"Statistical Properties","url":"https://www.academia.edu/Documents/in/Statistical_Properties?f_ri=61227"},{"id":473797,"name":"Microstructures","url":"https://www.academia.edu/Documents/in/Microstructures?f_ri=61227"},{"id":741144,"name":"Agent Based Model","url":"https://www.academia.edu/Documents/in/Agent_Based_Model?f_ri=61227"},{"id":892890,"name":"Point of View","url":"https://www.academia.edu/Documents/in/Point_of_View?f_ri=61227"},{"id":1820548,"name":"Minority Game","url":"https://www.academia.edu/Documents/in/Minority_Game?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_18580548" data-work_id="18580548" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/18580548/Econophysics_Empirical_facts_and_agent_based_models">Econophysics: Empirical facts and agent-based models</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This article aim at reviewing recent empirical and theoretical developments usually grouped under the term Econophysics. Since its name was coined in 1995 by merging the words "Economics" and "Physics", this new interdisciplinary field... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_18580548" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This article aim at reviewing recent empirical and theoretical developments usually grouped under the term Econophysics. Since its name was coined in 1995 by merging the words "Economics" and "Physics", this new interdisciplinary field has grown in various directions: theoretical macroeconomics (wealth distributions), microstructure of financial markets (order book modelling), econometrics of financial bubbles and crashes, etc. In the first part of the review, we begin with discussions on the interactions between Physics, Mathematics, Economics and Finance that led to the emergence of Econophysics. Then we present empirical studies revealing statistical properties of financial time series. We begin the presentation with the widely acknowledged "stylized facts" which describe the returns of financial assets -fat tails, volatility clustering, autocorrelation, etc. -and recall that some of these properties are directly linked to the way "time" is taken into account. We continue with the statistical properties observed on order books in financial markets. For the sake of illustrating this review, (nearly) all the stated facts are reproduced using our own high-frequency financial database. Finally, contributions to the study of correlations of assets such as random matrix theory and graph theory are presented. In the second part of the review, we deal with models in Econophysics through the point of view of agent-based modelling. Amongst a large number of multi-agent-based models, we have identified three representative areas. First, using previous work originally presented in the fields of behavioural finance and market microstructure theory, econophysicists have developed agent-based models of order-driven markets that are extensively presented here. Second, kinetic theory models designed to explain some empirical facts on wealth distribution are reviewed. Third, we briefly summarize game theory models by reviewing the now classic minority game and related problems.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/18580548" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0d7f5aac3b0dc4cdad6c5a68684072f6" rel="nofollow" data-download="{"attachment_id":40140011,"asset_id":18580548,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/40140011/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="38619200" href="https://independent.academia.edu/Fr%C3%A9d%C3%A9ricAbergel">Frédéric Abergel</a><script data-card-contents-for-user="38619200" type="text/json">{"id":38619200,"first_name":"Frédéric","last_name":"Abergel","domain_name":"independent","page_name":"FrédéricAbergel","display_name":"Frédéric Abergel","profile_url":"https://independent.academia.edu/Fr%C3%A9d%C3%A9ricAbergel?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_18580548 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="18580548"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 18580548, container: ".js-paper-rank-work_18580548", }); });</script></li><li class="js-percentile-work_18580548 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 18580548; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_18580548"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_18580548 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="18580548"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 18580548; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=18580548]").text(description); $(".js-view-count-work_18580548").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_18580548").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="18580548"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">16</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="757" rel="nofollow" href="https://www.academia.edu/Documents/in/Game_Theory">Game Theory</a>, <script data-card-contents-for-ri="757" type="text/json">{"id":757,"name":"Game Theory","url":"https://www.academia.edu/Documents/in/Game_Theory?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2616" rel="nofollow" href="https://www.academia.edu/Documents/in/Graph_Theory">Graph Theory</a>, <script data-card-contents-for-ri="2616" type="text/json">{"id":2616,"name":"Graph Theory","url":"https://www.academia.edu/Documents/in/Graph_Theory?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="7293" rel="nofollow" href="https://www.academia.edu/Documents/in/Market_Microstructure">Market Microstructure</a>, <script data-card-contents-for-ri="7293" type="text/json">{"id":7293,"name":"Market Microstructure","url":"https://www.academia.edu/Documents/in/Market_Microstructure?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="25221" rel="nofollow" href="https://www.academia.edu/Documents/in/Wealth_Distribution">Wealth Distribution</a><script data-card-contents-for-ri="25221" type="text/json">{"id":25221,"name":"Wealth Distribution","url":"https://www.academia.edu/Documents/in/Wealth_Distribution?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=18580548]'), work: {"id":18580548,"title":"Econophysics: Empirical facts and agent-based models","created_at":"2015-11-18T06:10:55.510-08:00","url":"https://www.academia.edu/18580548/Econophysics_Empirical_facts_and_agent_based_models?f_ri=61227","dom_id":"work_18580548","summary":"This article aim at reviewing recent empirical and theoretical developments usually grouped under the term Econophysics. Since its name was coined in 1995 by merging the words \"Economics\" and \"Physics\", this new interdisciplinary field has grown in various directions: theoretical macroeconomics (wealth distributions), microstructure of financial markets (order book modelling), econometrics of financial bubbles and crashes, etc. In the first part of the review, we begin with discussions on the interactions between Physics, Mathematics, Economics and Finance that led to the emergence of Econophysics. Then we present empirical studies revealing statistical properties of financial time series. We begin the presentation with the widely acknowledged \"stylized facts\" which describe the returns of financial assets -fat tails, volatility clustering, autocorrelation, etc. -and recall that some of these properties are directly linked to the way \"time\" is taken into account. We continue with the statistical properties observed on order books in financial markets. For the sake of illustrating this review, (nearly) all the stated facts are reproduced using our own high-frequency financial database. Finally, contributions to the study of correlations of assets such as random matrix theory and graph theory are presented. In the second part of the review, we deal with models in Econophysics through the point of view of agent-based modelling. Amongst a large number of multi-agent-based models, we have identified three representative areas. First, using previous work originally presented in the fields of behavioural finance and market microstructure theory, econophysicists have developed agent-based models of order-driven markets that are extensively presented here. Second, kinetic theory models designed to explain some empirical facts on wealth distribution are reviewed. Third, we briefly summarize game theory models by reviewing the now classic minority game and related problems.","downloadable_attachments":[{"id":40140011,"asset_id":18580548,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":38619200,"first_name":"Frédéric","last_name":"Abergel","domain_name":"independent","page_name":"FrédéricAbergel","display_name":"Frédéric Abergel","profile_url":"https://independent.academia.edu/Fr%C3%A9d%C3%A9ricAbergel?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":757,"name":"Game Theory","url":"https://www.academia.edu/Documents/in/Game_Theory?f_ri=61227","nofollow":true},{"id":2616,"name":"Graph Theory","url":"https://www.academia.edu/Documents/in/Graph_Theory?f_ri=61227","nofollow":true},{"id":7293,"name":"Market Microstructure","url":"https://www.academia.edu/Documents/in/Market_Microstructure?f_ri=61227","nofollow":true},{"id":25221,"name":"Wealth Distribution","url":"https://www.academia.edu/Documents/in/Wealth_Distribution?f_ri=61227","nofollow":true},{"id":48458,"name":"High Frequency","url":"https://www.academia.edu/Documents/in/High_Frequency?f_ri=61227"},{"id":56761,"name":"Agent Based Modelling","url":"https://www.academia.edu/Documents/in/Agent_Based_Modelling?f_ri=61227"},{"id":59051,"name":"Kinetic Theory","url":"https://www.academia.edu/Documents/in/Kinetic_Theory?f_ri=61227"},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227"},{"id":137277,"name":"Random Matrix Theory","url":"https://www.academia.edu/Documents/in/Random_Matrix_Theory?f_ri=61227"},{"id":219474,"name":"Empirical Study","url":"https://www.academia.edu/Documents/in/Empirical_Study?f_ri=61227"},{"id":270673,"name":"Financial Market","url":"https://www.academia.edu/Documents/in/Financial_Market?f_ri=61227"},{"id":428833,"name":"Statistical Properties","url":"https://www.academia.edu/Documents/in/Statistical_Properties?f_ri=61227"},{"id":473797,"name":"Microstructures","url":"https://www.academia.edu/Documents/in/Microstructures?f_ri=61227"},{"id":741144,"name":"Agent Based Model","url":"https://www.academia.edu/Documents/in/Agent_Based_Model?f_ri=61227"},{"id":892890,"name":"Point of View","url":"https://www.academia.edu/Documents/in/Point_of_View?f_ri=61227"},{"id":1820548,"name":"Minority Game","url":"https://www.academia.edu/Documents/in/Minority_Game?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_37180199 coauthored" data-work_id="37180199" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/37180199/DESENVOLVIMENTO_DE_UM_MODELO_ADAPTATIVO_BASEADO_EM_UM_SISTEMA_SVR_WAVELET_HI_BRIDO_PARA_PREVISA_O_DE_SE_RIES_TEMPORAIS_FINANCEIRAS">DESENVOLVIMENTO DE UM MODELO ADAPTATIVO BASEADO EM UM SISTEMA SVR-WAVELET HÍBRIDO PARA PREVISÃO DE SÉRIES TEMPORAIS FINANCEIRAS</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The necessity to anticipate and identify changes in events points to a new direction in the stock exchange market and reaches the analysis of the oscillations of prices of financial assets. This necessity leads to an argument about new... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_37180199" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The necessity to anticipate and identify changes in events points to a new direction in the stock exchange market and reaches the analysis of the oscillations of prices of financial assets. This necessity leads to an argument about new alternatives in the prediction of financial time series using machine learning methods. Several models have been developed to perform the analysis and prediction of financial asset data. This thesis aims to propose the development of SVR-wavelet model, an adaptive and hybrid prediction model, which integrates wavelet models and Support Vector Regression (SVR), for prediction of Financial Time Series, particularly Foreign Exchange Market (FOREX), obtained from a public knowledge base. The method consists of using the Discrete Wavelets Transform (DWT) to decompose data from FOREX time series, that are used as SVR input variables to predict new data. The series are adjusted by generating new predictions of the original series, which are compared with other traditional models such as the Autoregressive Integrated Moving Average model (ARIMA), the Autoregressive Fractionally Integrated Moving Average model (ARFIMA), the Generalized Autoregressive Conditional Heteroskedasticity model (GARCH) and the traditional SVR model with Kernel. In addition, normality and unit root tests for non-linear distribution, and correlation tests, are performed to verify that the FOREX time series are adequate for the verification of SVR-wavelet hybrid model and comparison with traditional models. There is also the adherence to the Hurst Exponent through the statistical Rescaled Range (R/S).</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/37180199" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="2ae56814c2ee9c9c93dbe8e25a2cef91" rel="nofollow" data-download="{"attachment_id":57131549,"asset_id":37180199,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/57131549/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="7903320" href="https://usp-br.academia.edu/MiltonRaimundo">Milton Raimundo</a><script data-card-contents-for-user="7903320" type="text/json">{"id":7903320,"first_name":"Milton","last_name":"Raimundo","domain_name":"usp-br","page_name":"MiltonRaimundo","display_name":"Milton Raimundo","profile_url":"https://usp-br.academia.edu/MiltonRaimundo?f_ri=61227","photo":"https://0.academia-photos.com/7903320/6373001/19909857/s65_milton.raimundo.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-37180199">+1</span><div class="hidden js-additional-users-37180199"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://usp-br.academia.edu/JunOkamotoJr">Jun Okamoto Jr.</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-37180199'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-37180199').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_37180199 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="37180199"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 37180199, container: ".js-paper-rank-work_37180199", }); });</script></li><li class="js-percentile-work_37180199 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 37180199; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_37180199"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_37180199 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="37180199"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 37180199; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=37180199]").text(description); $(".js-view-count-work_37180199").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_37180199").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="37180199"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="465" rel="nofollow" href="https://www.academia.edu/Documents/in/Artificial_Intelligence">Artificial Intelligence</a>, <script data-card-contents-for-ri="465" type="text/json">{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="61227" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_time_series">Financial time series</a><script data-card-contents-for-ri="61227" type="text/json">{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=37180199]'), work: {"id":37180199,"title":"DESENVOLVIMENTO DE UM MODELO ADAPTATIVO BASEADO EM UM SISTEMA SVR-WAVELET HÍBRIDO PARA PREVISÃO DE SÉRIES TEMPORAIS FINANCEIRAS","created_at":"2018-08-04T08:20:27.725-07:00","url":"https://www.academia.edu/37180199/DESENVOLVIMENTO_DE_UM_MODELO_ADAPTATIVO_BASEADO_EM_UM_SISTEMA_SVR_WAVELET_HI_BRIDO_PARA_PREVISA_O_DE_SE_RIES_TEMPORAIS_FINANCEIRAS?f_ri=61227","dom_id":"work_37180199","summary":"The necessity to anticipate and identify changes in events points to a new direction in the stock exchange market and reaches the analysis of the oscillations of prices of financial assets. This necessity leads to an argument about new alternatives in the prediction of financial time series using machine learning methods. Several models have been developed to perform the analysis and prediction of financial asset data. This thesis aims to propose the development of SVR-wavelet model, an adaptive and hybrid prediction model, which integrates wavelet models and Support Vector Regression (SVR), for prediction of Financial Time Series, particularly Foreign Exchange Market (FOREX), obtained from a public knowledge base. The method consists of using the Discrete Wavelets Transform (DWT) to decompose data from FOREX time series, that are used as SVR input variables to predict new data. The series are adjusted by generating new predictions of the original series, which are compared with other traditional models such as the Autoregressive Integrated Moving Average model (ARIMA), the Autoregressive Fractionally Integrated Moving Average model (ARFIMA), the Generalized Autoregressive Conditional Heteroskedasticity model (GARCH) and the traditional SVR model with Kernel. In addition, normality and unit root tests for non-linear distribution, and correlation tests, are performed to verify that the FOREX time series are adequate for the verification of SVR-wavelet hybrid model and comparison with traditional models. There is also the adherence to the Hurst Exponent through the statistical Rescaled Range (R/S).","downloadable_attachments":[{"id":57131549,"asset_id":37180199,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":7903320,"first_name":"Milton","last_name":"Raimundo","domain_name":"usp-br","page_name":"MiltonRaimundo","display_name":"Milton Raimundo","profile_url":"https://usp-br.academia.edu/MiltonRaimundo?f_ri=61227","photo":"https://0.academia-photos.com/7903320/6373001/19909857/s65_milton.raimundo.png"},{"id":52725198,"first_name":"Jun","last_name":"Okamoto Jr.","domain_name":"usp-br","page_name":"JunOkamotoJr","display_name":"Jun Okamoto Jr.","profile_url":"https://usp-br.academia.edu/JunOkamotoJr?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=61227","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_27346048" data-work_id="27346048" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/27346048/Alternative_Asymmetric_Stochastic_Volatility_Models">Alternative Asymmetric Stochastic Volatility Models</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The stochastic volatility model usually incorporates asymmetric effects by introducing the negative correlation between the innovations in returns and volatility. In this paper, we propose a new asymmetric stochastic volatility model,... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_27346048" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The stochastic volatility model usually incorporates asymmetric effects by introducing the negative correlation between the innovations in returns and volatility. In this paper, we propose a new asymmetric stochastic volatility model, based on the leverage and size effects. The model is a generalization of the exponential GARCH (EGARCH) model of . We consider categories for asymmetric effects, which describes the difference among the asymmetric effect of the EGARCH model, the threshold effects indicator function of Glosten, Jagannathan and Runkle (1992), and the negative correlation between the innovations in returns and volatility. The new model is estimated by the efficient importance sampling method of Liesenfeld and Richard (2003), and the finite sample properties of the estimator are investigated using numerical simulations. Four financial time series are used to estimate the alternative asymmetric SV models, with empirical asymmetric effects found to be statistically significant in each case. The empirical results for S&P 500 and Yen/USD returns indicate that the leverage and size effects are significant, supporting the general model.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/27346048" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="4b43cda2f9c18f9b29ff3822f9a09f71" rel="nofollow" data-download="{"attachment_id":47600889,"asset_id":27346048,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/47600889/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="34286711" href="https://nthu.academia.edu/MichaelMcAleer">Michael McAleer</a><script data-card-contents-for-user="34286711" type="text/json">{"id":34286711,"first_name":"Michael","last_name":"McAleer","domain_name":"nthu","page_name":"MichaelMcAleer","display_name":"Michael McAleer","profile_url":"https://nthu.academia.edu/MichaelMcAleer?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_27346048 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="27346048"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 27346048, container: ".js-paper-rank-work_27346048", }); });</script></li><li class="js-percentile-work_27346048 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 27346048; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_27346048"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_27346048 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="27346048"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 27346048; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=27346048]").text(description); $(".js-view-count-work_27346048").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_27346048").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="27346048"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">12</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="747" rel="nofollow" href="https://www.academia.edu/Documents/in/Econometrics">Econometrics</a>, <script data-card-contents-for-ri="747" type="text/json">{"id":747,"name":"Econometrics","url":"https://www.academia.edu/Documents/in/Econometrics?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="60658" rel="nofollow" href="https://www.academia.edu/Documents/in/Numerical_Simulation">Numerical Simulation</a>, <script data-card-contents-for-ri="60658" type="text/json">{"id":60658,"name":"Numerical Simulation","url":"https://www.academia.edu/Documents/in/Numerical_Simulation?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="61227" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_time_series">Financial time series</a>, <script data-card-contents-for-ri="61227" type="text/json">{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="87533" rel="nofollow" href="https://www.academia.edu/Documents/in/Stochastic_Volatility">Stochastic Volatility</a><script data-card-contents-for-ri="87533" type="text/json">{"id":87533,"name":"Stochastic Volatility","url":"https://www.academia.edu/Documents/in/Stochastic_Volatility?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=27346048]'), work: {"id":27346048,"title":"Alternative Asymmetric Stochastic Volatility Models","created_at":"2016-07-28T13:02:27.473-07:00","url":"https://www.academia.edu/27346048/Alternative_Asymmetric_Stochastic_Volatility_Models?f_ri=61227","dom_id":"work_27346048","summary":"The stochastic volatility model usually incorporates asymmetric effects by introducing the negative correlation between the innovations in returns and volatility. In this paper, we propose a new asymmetric stochastic volatility model, based on the leverage and size effects. The model is a generalization of the exponential GARCH (EGARCH) model of . We consider categories for asymmetric effects, which describes the difference among the asymmetric effect of the EGARCH model, the threshold effects indicator function of Glosten, Jagannathan and Runkle (1992), and the negative correlation between the innovations in returns and volatility. The new model is estimated by the efficient importance sampling method of Liesenfeld and Richard (2003), and the finite sample properties of the estimator are investigated using numerical simulations. Four financial time series are used to estimate the alternative asymmetric SV models, with empirical asymmetric effects found to be statistically significant in each case. The empirical results for S\u0026P 500 and Yen/USD returns indicate that the leverage and size effects are significant, supporting the general model.","downloadable_attachments":[{"id":47600889,"asset_id":27346048,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":34286711,"first_name":"Michael","last_name":"McAleer","domain_name":"nthu","page_name":"MichaelMcAleer","display_name":"Michael McAleer","profile_url":"https://nthu.academia.edu/MichaelMcAleer?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":747,"name":"Econometrics","url":"https://www.academia.edu/Documents/in/Econometrics?f_ri=61227","nofollow":true},{"id":60658,"name":"Numerical Simulation","url":"https://www.academia.edu/Documents/in/Numerical_Simulation?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true},{"id":87533,"name":"Stochastic Volatility","url":"https://www.academia.edu/Documents/in/Stochastic_Volatility?f_ri=61227","nofollow":true},{"id":125564,"name":"Statistical Significance","url":"https://www.academia.edu/Documents/in/Statistical_Significance?f_ri=61227"},{"id":167116,"name":"Leverage","url":"https://www.academia.edu/Documents/in/Leverage?f_ri=61227"},{"id":184685,"name":"Asymmetry","url":"https://www.academia.edu/Documents/in/Asymmetry?f_ri=61227"},{"id":307221,"name":"Importance Sampling","url":"https://www.academia.edu/Documents/in/Importance_Sampling?f_ri=61227"},{"id":489225,"name":"Stock Price","url":"https://www.academia.edu/Documents/in/Stock_Price?f_ri=61227"},{"id":526858,"name":"Size Effect","url":"https://www.academia.edu/Documents/in/Size_Effect?f_ri=61227"},{"id":749302,"name":"Indexation","url":"https://www.academia.edu/Documents/in/Indexation?f_ri=61227"},{"id":2413807,"name":"Generic model","url":"https://www.academia.edu/Documents/in/Generic_model?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_12404869" data-work_id="12404869" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/12404869/Econophysics_complex_correlations_and_trend_switchings_in_financial_time_series">Econophysics—complex correlations and trend switchings in financial time series</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This article focuses on the analysis of financial time series and their correlations. A method is used for quantifying pattern based correlations of a time series. With this methodology, evidence is found that typical behavioral patterns... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_12404869" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This article focuses on the analysis of financial time series and their correlations. A method is used for quantifying pattern based correlations of a time series. With this methodology, evidence is found that typical behavioral patterns of financial market participants manifest over short time scales, i.e., that reactions to given price patterns are not entirely random, but that similar price patterns also cause similar reactions. Based on the investigation of the complex correlations in financial time series, the question arises, which properties change when switching from a positive trend to a negative trend. An empirical quantification by rescaling provides the result that new price extrema coincide with a significant increase in transaction volume and a significant decrease in the length of corresponding time intervals between transactions. These findings are independent of the time scale over 9 orders of magnitude, and they exhibit characteristics which one can also find in other complex systems in nature (and in physical systems in particular). These properties are independent of the markets analyzed. Trends that exist only for a few seconds show the same characteristics as trends on time scales of several months. Thus, it is possible to study financial bubbles and their collapses in more detail, because trend switching processes occur with higher frequency on small time scales. In addition, a Monte Carlo based simulation of financial markets is analyzed and extended in order to reproduce empirical features and to gain insight into their causes. These causes include both financial market microstructure and the risk aversion of market participants.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/12404869" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f3d3be825c701a972ce9e85878afe4f6" rel="nofollow" data-download="{"attachment_id":46205421,"asset_id":12404869,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/46205421/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="31170267" href="https://warwick.academia.edu/TobiasPreis">Tobias Preis</a><script data-card-contents-for-user="31170267" type="text/json">{"id":31170267,"first_name":"Tobias","last_name":"Preis","domain_name":"warwick","page_name":"TobiasPreis","display_name":"Tobias Preis","profile_url":"https://warwick.academia.edu/TobiasPreis?f_ri=61227","photo":"https://0.academia-photos.com/31170267/9157329/10212093/s65_tobias.preis.jpg"}</script></span></span></li><li class="js-paper-rank-work_12404869 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="12404869"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 12404869, container: ".js-paper-rank-work_12404869", }); });</script></li><li class="js-percentile-work_12404869 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12404869; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_12404869"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_12404869 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="12404869"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12404869; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=12404869]").text(description); $(".js-view-count-work_12404869").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_12404869").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="12404869"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">17</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="2161" rel="nofollow" href="https://www.academia.edu/Documents/in/Microstructure">Microstructure</a>, <script data-card-contents-for-ri="2161" type="text/json">{"id":2161,"name":"Microstructure","url":"https://www.academia.edu/Documents/in/Microstructure?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4456" rel="nofollow" href="https://www.academia.edu/Documents/in/Time_Series">Time Series</a>, <script data-card-contents-for-ri="4456" type="text/json">{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="6177" rel="nofollow" href="https://www.academia.edu/Documents/in/Modeling">Modeling</a>, <script data-card-contents-for-ri="6177" type="text/json">{"id":6177,"name":"Modeling","url":"https://www.academia.edu/Documents/in/Modeling?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="6974" rel="nofollow" href="https://www.academia.edu/Documents/in/Monte_Carlo">Monte Carlo</a><script data-card-contents-for-ri="6974" type="text/json">{"id":6974,"name":"Monte Carlo","url":"https://www.academia.edu/Documents/in/Monte_Carlo?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=12404869]'), work: {"id":12404869,"title":"Econophysics—complex correlations and trend switchings in financial time series","created_at":"2015-05-15T16:02:42.288-07:00","url":"https://www.academia.edu/12404869/Econophysics_complex_correlations_and_trend_switchings_in_financial_time_series?f_ri=61227","dom_id":"work_12404869","summary":"This article focuses on the analysis of financial time series and their correlations. A method is used for quantifying pattern based correlations of a time series. With this methodology, evidence is found that typical behavioral patterns of financial market participants manifest over short time scales, i.e., that reactions to given price patterns are not entirely random, but that similar price patterns also cause similar reactions. Based on the investigation of the complex correlations in financial time series, the question arises, which properties change when switching from a positive trend to a negative trend. An empirical quantification by rescaling provides the result that new price extrema coincide with a significant increase in transaction volume and a significant decrease in the length of corresponding time intervals between transactions. These findings are independent of the time scale over 9 orders of magnitude, and they exhibit characteristics which one can also find in other complex systems in nature (and in physical systems in particular). These properties are independent of the markets analyzed. Trends that exist only for a few seconds show the same characteristics as trends on time scales of several months. Thus, it is possible to study financial bubbles and their collapses in more detail, because trend switching processes occur with higher frequency on small time scales. In addition, a Monte Carlo based simulation of financial markets is analyzed and extended in order to reproduce empirical features and to gain insight into their causes. These causes include both financial market microstructure and the risk aversion of market participants.","downloadable_attachments":[{"id":46205421,"asset_id":12404869,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":31170267,"first_name":"Tobias","last_name":"Preis","domain_name":"warwick","page_name":"TobiasPreis","display_name":"Tobias Preis","profile_url":"https://warwick.academia.edu/TobiasPreis?f_ri=61227","photo":"https://0.academia-photos.com/31170267/9157329/10212093/s65_tobias.preis.jpg"}],"research_interests":[{"id":2161,"name":"Microstructure","url":"https://www.academia.edu/Documents/in/Microstructure?f_ri=61227","nofollow":true},{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true},{"id":6177,"name":"Modeling","url":"https://www.academia.edu/Documents/in/Modeling?f_ri=61227","nofollow":true},{"id":6974,"name":"Monte Carlo","url":"https://www.academia.edu/Documents/in/Monte_Carlo?f_ri=61227","nofollow":true},{"id":13242,"name":"Market Structure","url":"https://www.academia.edu/Documents/in/Market_Structure?f_ri=61227"},{"id":54501,"name":"Complex System","url":"https://www.academia.edu/Documents/in/Complex_System?f_ri=61227"},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227"},{"id":69827,"name":"Risk Aversion","url":"https://www.academia.edu/Documents/in/Risk_Aversion?f_ri=61227"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences?f_ri=61227"},{"id":118582,"name":"Physical sciences","url":"https://www.academia.edu/Documents/in/Physical_sciences?f_ri=61227"},{"id":270673,"name":"Financial Market","url":"https://www.academia.edu/Documents/in/Financial_Market?f_ri=61227"},{"id":372874,"name":"Transaction Processing","url":"https://www.academia.edu/Documents/in/Transaction_Processing?f_ri=61227"},{"id":405713,"name":"Collapse","url":"https://www.academia.edu/Documents/in/Collapse?f_ri=61227"},{"id":473797,"name":"Microstructures","url":"https://www.academia.edu/Documents/in/Microstructures?f_ri=61227"},{"id":733224,"name":"Empirical Method","url":"https://www.academia.edu/Documents/in/Empirical_Method?f_ri=61227"},{"id":1333436,"name":"Monte Carlo Method","url":"https://www.academia.edu/Documents/in/Monte_Carlo_Method?f_ri=61227"},{"id":1475619,"name":"Switching Time","url":"https://www.academia.edu/Documents/in/Switching_Time?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_37887771" data-work_id="37887771" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/37887771/PAPER_DETERMINANTS_OF_MANUFACTURING_SECTOR_GROWTH_IN_NIGERIA">PAPER-DETERMINANTS OF MANUFACTURING SECTOR GROWTH IN NIGERIA</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This study examines the determinants of manufacturing sector growth in Nigeria within the time frame of 1981-2016. The main objective of this study is to identify the determinants of the manufacturing sector growth in Nigeria. A model was... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_37887771" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This study examines the determinants of manufacturing sector growth in Nigeria within the time frame of 1981-2016. The main objective of this study is to identify the determinants of the manufacturing sector growth in Nigeria. A model was constructed to incorporate manufacturing output (MO) as the dependent variable, federal government allocation on power, inflation rate, trade openness, gross capital formation and primary school enrolment as the independent variables and tested using the ordinary least-square (OLS) techniques. The empirical result shows that federal government allocation on power, inflation rate and primary school enrolment have negative relationship on the manufacturing output whereas trade openness and gross capital formation have positive relationship on manufacturing output in Nigeria. From the result, federal government allocation on power, trade openness and gross capital formation are significant variables to determine manufacturing output in Nigeria whereas inflation rate and primary school enrolment are not significant variables to determine manufacturing output in Nigeria. Based on the result, the researcher recommends that Nigeria should based on reckoning diversify the income stream of the economy (rebasing the economy) so as to lower the production cost which will based on multiplier effect decrease the selling price of the domestic product.<br /><br />Keywords: Domestic product, Manufacturing output, Inflation trade, Trade Openness,</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/37887771" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="80e2bfa4027c10718f36db0930c56416" rel="nofollow" data-download="{"attachment_id":57895553,"asset_id":37887771,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/57895553/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="27682898" href="https://independent.academia.edu/UDECHUKWUCHIDOZIEANTHONY">UDECHUKWU CHIDOZIE ANTHONY</a><script data-card-contents-for-user="27682898" type="text/json">{"id":27682898,"first_name":"UDECHUKWU","last_name":"CHIDOZIE ANTHONY","domain_name":"independent","page_name":"UDECHUKWUCHIDOZIEANTHONY","display_name":"UDECHUKWU CHIDOZIE ANTHONY","profile_url":"https://independent.academia.edu/UDECHUKWUCHIDOZIEANTHONY?f_ri=61227","photo":"https://0.academia-photos.com/27682898/15585073/17347376/s65_udechukwu.chidozie_anthony.jpg"}</script></span></span></li><li class="js-paper-rank-work_37887771 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="37887771"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 37887771, container: ".js-paper-rank-work_37887771", }); 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The main objective of this study is to identify the determinants of the manufacturing sector growth in Nigeria. A model was constructed to incorporate manufacturing output (MO) as the dependent variable, federal government allocation on power, inflation rate, trade openness, gross capital formation and primary school enrolment as the independent variables and tested using the ordinary least-square (OLS) techniques. The empirical result shows that federal government allocation on power, inflation rate and primary school enrolment have negative relationship on the manufacturing output whereas trade openness and gross capital formation have positive relationship on manufacturing output in Nigeria. From the result, federal government allocation on power, trade openness and gross capital formation are significant variables to determine manufacturing output in Nigeria whereas inflation rate and primary school enrolment are not significant variables to determine manufacturing output in Nigeria. Based on the result, the researcher recommends that Nigeria should based on reckoning diversify the income stream of the economy (rebasing the economy) so as to lower the production cost which will based on multiplier effect decrease the selling price of the domestic product.\n\nKeywords: Domestic product, Manufacturing output, Inflation trade, Trade Openness,\n","downloadable_attachments":[{"id":57895553,"asset_id":37887771,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":27682898,"first_name":"UDECHUKWU","last_name":"CHIDOZIE ANTHONY","domain_name":"independent","page_name":"UDECHUKWUCHIDOZIEANTHONY","display_name":"UDECHUKWU CHIDOZIE ANTHONY","profile_url":"https://independent.academia.edu/UDECHUKWUCHIDOZIEANTHONY?f_ri=61227","photo":"https://0.academia-photos.com/27682898/15585073/17347376/s65_udechukwu.chidozie_anthony.jpg"}],"research_interests":[{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true},{"id":8995,"name":"Nonlinear Time Series","url":"https://www.academia.edu/Documents/in/Nonlinear_Time_Series?f_ri=61227","nofollow":true},{"id":17377,"name":"Fuzzy Time Series","url":"https://www.academia.edu/Documents/in/Fuzzy_Time_Series?f_ri=61227","nofollow":true},{"id":30485,"name":"Time series analysis","url":"https://www.academia.edu/Documents/in/Time_series_analysis?f_ri=61227","nofollow":true},{"id":44667,"name":"Time Series Data Mining","url":"https://www.academia.edu/Documents/in/Time_Series_Data_Mining?f_ri=61227"},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227"},{"id":108547,"name":"Financial End Economic Time Series","url":"https://www.academia.edu/Documents/in/Financial_End_Economic_Time_Series?f_ri=61227"},{"id":151059,"name":"Time-Series","url":"https://www.academia.edu/Documents/in/Time-Series?f_ri=61227"},{"id":165383,"name":"Time Series Forecasting , Neural Network","url":"https://www.academia.edu/Documents/in/Time_Series_Forecasting_Neural_Network?f_ri=61227"},{"id":181785,"name":"Time Series Data","url":"https://www.academia.edu/Documents/in/Time_Series_Data?f_ri=61227"},{"id":366369,"name":"Time Series Forecasting","url":"https://www.academia.edu/Documents/in/Time_Series_Forecasting?f_ri=61227"},{"id":433941,"name":"Nonlinear Time Series Analysis","url":"https://www.academia.edu/Documents/in/Nonlinear_Time_Series_Analysis?f_ri=61227"},{"id":461027,"name":"Panel Time Series","url":"https://www.academia.edu/Documents/in/Panel_Time_Series?f_ri=61227"},{"id":495206,"name":"Applied Time Series Econometrics","url":"https://www.academia.edu/Documents/in/Applied_Time_Series_Econometrics?f_ri=61227"},{"id":742747,"name":"Bilinear Time Series","url":"https://www.academia.edu/Documents/in/Bilinear_Time_Series?f_ri=61227"},{"id":770322,"name":"Chaotic Time Series","url":"https://www.academia.edu/Documents/in/Chaotic_Time_Series?f_ri=61227"},{"id":1003619,"name":"Time Series Analysis and Forecasting","url":"https://www.academia.edu/Documents/in/Time_Series_Analysis_and_Forecasting?f_ri=61227"},{"id":1034460,"name":"Nonlinear Time series Models","url":"https://www.academia.edu/Documents/in/Nonlinear_Time_series_Models?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_35038602 coauthored" data-work_id="35038602" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/35038602/Modeling_Volatility_in_Nigeria_Foreign_Exchange_Market_Using_GARCH_type_Models">Modeling Volatility in Nigeria Foreign Exchange Market Using GARCH-type Models</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this study, the performance of GARCH-type model is considered in modelling Nigeria foreign exchange returns. The datasets consists of the foreign exchange of Nigeria naira for the periods before recession and during recession. It is... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_35038602" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this study, the performance of GARCH-type model is considered in modelling Nigeria foreign exchange returns. The datasets consists of the foreign exchange of Nigeria naira for the periods before recession and during recession. It is observed that volatility is higher during recession than when there was no recession. Model selection criteria based on Hannan-Quinn Information Criterion (HQIC) shows that Gaussian process is least considered model to capture the variability in foreign exchange rate returns in Nigeria, but student's t and Generalized Error distribution are more suitable, therefore forecast performance was used to access each of the Asymmetric models. The empirical analysis shows that GARCH (1, 1) and gjrGARCH (1, 1) with Student's t error distribution and iGARCH(1, 1), sGARCH(1,1), and csGARCH (1,1) are the best fitted models. Fifty days out-of-sample forecast shows that csGARCH (1, 1) based on Generalized Error distribution is the best predictive model based on Mean Square Error (MSE), and sGARCH based on Mean Absolute Error (MAE) and Directional Absolute Error (DAE). The study recommends that future study should consider alternative error distributions with a view to realizing a more robust volatility forecasting model that could guarantee sound policy choices. 1. Introduction Financial time series data often consist of periods of calm behaviour alternating with periods of very wild fluctuations. The study on the volatility of exchange rate is closely linked to the risk of assets, as volatility measures exposure to risk. Higher volatility leads to large variations of return, hence higher risk. Volatility of exchange rate provide useful information in measuring risk, and a number models are applied in forecasting exchange rate movement and evaluating the performance of the local currencies in international market. Statement made by (Hamadu and Adeleke 2009) which cannot be ignored is that forecasting currency exchange rate rates is an important financial problem that has recorded a great deal of attention particularly because of intrinsic difficulty and practical applications. The issue of modelling exchange rate volatility has gained considerable importance in the research studies since 1973, when many countries shifted towards floating exchange rate from fixed exchange rate regime. Part of the studies were conducted to understand the behaviour of exchange rate and to explain the sources of its movements and fluctuations. There has been excessive volatility of the Nigeria Naira against major foreign currencies in the exchange market since the adoption of flexible exchange–rate regimes in 1986. Therefore, continuous exchange rate volatility was thought to have led to currency crises, distortion of production patterns as well as sharp fluctuations in external reserve (Bala and Asemota 2013). Exchange rate volatility is a major challenge facing development of an economy, making planning more problematic and investment more risky. Nigeria being a developing nation highly dependent on foreign trade, and these trades relies on exchange rate. This show that the impact of exchange rate variability on economies especially developing ones is not only in one direction. Many studies have adopted diverse techniques in modelling exchange rate volatility. Hamadu and Adeleke (2009) modelled and compared Multilayer Perception Back Propagation Neural Network (MLPBPNN) model with several models, along with ARIMA generated by Expert Modeler System (EMS) to model Nigerian foreign exchange while Adeleke et. al., (2015) modelled daily exchange rate using extreme value theory, among others. In modelling volatility, popular and frequently applied models to estimate exchange rate volatility are the autoregressive conditional heteroscedastic (ARCH) model advanced by Engle (1982) and generalized ARCH (GARCH) model developed independently by Bollerslev (1986) and Taylor (1987) considered to be symmetric. Extension of the symmetric GARCH is the like of EGARCH, IGARCH, TGARCH, fGARCH, GJRGARCH, CSGARCH, TGARCH, among others. The GARCH-type model is a popular type of model being used to model stock and exchange rate volatility. Lim and Sek (2013) used both GARCH-types to model and identify the superior model in capturing the characteristics of stock market at different type. In the recent times, the GARCH-type of models has been adopted in various capacities. Hu and Tsay (2014) consider a sample estimate of generalized kurtosis matrix and proposed test statistics for detecting linear combinations that do not have conditional heteroscedascity, they applied the test to weekly log returns of seven exchange rates against US dollars. Kalli and Griffin (2015) proposed stochastic Volatility (SV) model drawing strength from auto-regressive SV models, aggregation of auto-regressive process, and Bayesian non-parametric</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/35038602" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b3e331be14191f50bb6139baec70f4e6" rel="nofollow" data-download="{"attachment_id":54900868,"asset_id":35038602,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/54900868/download_file?st=MTc0MDU3MTkzMCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="3522766" href="https://run.academia.edu/DrOLUMIDEADESINA">Dr. OLUMIDE S ADESINA</a><script data-card-contents-for-user="3522766" type="text/json">{"id":3522766,"first_name":"Dr. OLUMIDE","last_name":"ADESINA","domain_name":"run","page_name":"DrOLUMIDEADESINA","display_name":"Dr. OLUMIDE S ADESINA","profile_url":"https://run.academia.edu/DrOLUMIDEADESINA?f_ri=61227","photo":"https://0.academia-photos.com/3522766/6189170/24702925/s65_dr._olumide.adesina.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-35038602">+1</span><div class="hidden js-additional-users-35038602"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/OOLUWAPAMILERIN">OYEWOLE OLUWAPAMILERIN</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-35038602'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-35038602').html(); 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The datasets consists of the foreign exchange of Nigeria naira for the periods before recession and during recession. It is observed that volatility is higher during recession than when there was no recession. Model selection criteria based on Hannan-Quinn Information Criterion (HQIC) shows that Gaussian process is least considered model to capture the variability in foreign exchange rate returns in Nigeria, but student's t and Generalized Error distribution are more suitable, therefore forecast performance was used to access each of the Asymmetric models. The empirical analysis shows that GARCH (1, 1) and gjrGARCH (1, 1) with Student's t error distribution and iGARCH(1, 1), sGARCH(1,1), and csGARCH (1,1) are the best fitted models. Fifty days out-of-sample forecast shows that csGARCH (1, 1) based on Generalized Error distribution is the best predictive model based on Mean Square Error (MSE), and sGARCH based on Mean Absolute Error (MAE) and Directional Absolute Error (DAE). The study recommends that future study should consider alternative error distributions with a view to realizing a more robust volatility forecasting model that could guarantee sound policy choices. 1. Introduction Financial time series data often consist of periods of calm behaviour alternating with periods of very wild fluctuations. The study on the volatility of exchange rate is closely linked to the risk of assets, as volatility measures exposure to risk. Higher volatility leads to large variations of return, hence higher risk. Volatility of exchange rate provide useful information in measuring risk, and a number models are applied in forecasting exchange rate movement and evaluating the performance of the local currencies in international market. Statement made by (Hamadu and Adeleke 2009) which cannot be ignored is that forecasting currency exchange rate rates is an important financial problem that has recorded a great deal of attention particularly because of intrinsic difficulty and practical applications. The issue of modelling exchange rate volatility has gained considerable importance in the research studies since 1973, when many countries shifted towards floating exchange rate from fixed exchange rate regime. Part of the studies were conducted to understand the behaviour of exchange rate and to explain the sources of its movements and fluctuations. There has been excessive volatility of the Nigeria Naira against major foreign currencies in the exchange market since the adoption of flexible exchange–rate regimes in 1986. Therefore, continuous exchange rate volatility was thought to have led to currency crises, distortion of production patterns as well as sharp fluctuations in external reserve (Bala and Asemota 2013). Exchange rate volatility is a major challenge facing development of an economy, making planning more problematic and investment more risky. Nigeria being a developing nation highly dependent on foreign trade, and these trades relies on exchange rate. This show that the impact of exchange rate variability on economies especially developing ones is not only in one direction. Many studies have adopted diverse techniques in modelling exchange rate volatility. Hamadu and Adeleke (2009) modelled and compared Multilayer Perception Back Propagation Neural Network (MLPBPNN) model with several models, along with ARIMA generated by Expert Modeler System (EMS) to model Nigerian foreign exchange while Adeleke et. al., (2015) modelled daily exchange rate using extreme value theory, among others. In modelling volatility, popular and frequently applied models to estimate exchange rate volatility are the autoregressive conditional heteroscedastic (ARCH) model advanced by Engle (1982) and generalized ARCH (GARCH) model developed independently by Bollerslev (1986) and Taylor (1987) considered to be symmetric. Extension of the symmetric GARCH is the like of EGARCH, IGARCH, TGARCH, fGARCH, GJRGARCH, CSGARCH, TGARCH, among others. The GARCH-type model is a popular type of model being used to model stock and exchange rate volatility. Lim and Sek (2013) used both GARCH-types to model and identify the superior model in capturing the characteristics of stock market at different type. In the recent times, the GARCH-type of models has been adopted in various capacities. Hu and Tsay (2014) consider a sample estimate of generalized kurtosis matrix and proposed test statistics for detecting linear combinations that do not have conditional heteroscedascity, they applied the test to weekly log returns of seven exchange rates against US dollars. Kalli and Griffin (2015) proposed stochastic Volatility (SV) model drawing strength from auto-regressive SV models, aggregation of auto-regressive process, and Bayesian non-parametric","downloadable_attachments":[{"id":54900868,"asset_id":35038602,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":3522766,"first_name":"Dr. OLUMIDE","last_name":"ADESINA","domain_name":"run","page_name":"DrOLUMIDEADESINA","display_name":"Dr. OLUMIDE S ADESINA","profile_url":"https://run.academia.edu/DrOLUMIDEADESINA?f_ri=61227","photo":"https://0.academia-photos.com/3522766/6189170/24702925/s65_dr._olumide.adesina.jpg"},{"id":71153434,"first_name":"OYEWOLE","last_name":"OLUWAPAMILERIN","domain_name":"independent","page_name":"OOLUWAPAMILERIN","display_name":"OYEWOLE OLUWAPAMILERIN","profile_url":"https://independent.academia.edu/OOLUWAPAMILERIN?f_ri=61227","photo":"https://0.academia-photos.com/71153434/18841069/18799744/s65_oyewole.oluwapamilerin.jpg"}],"research_interests":[{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true},{"id":100094,"name":"Bayesian statistics","url":"https://www.academia.edu/Documents/in/Bayesian_statistics?f_ri=61227","nofollow":true},{"id":2814568,"name":"Probability Distribution","url":"https://www.academia.edu/Documents/in/Probability_Distribution?f_ri=61227","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_15592666" data-work_id="15592666" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/15592666/Do_benchmark_African_equity_indices_exhibit_the_stylized_facts">Do benchmark African equity indices exhibit the stylized facts?</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper investigates if benchmark African equity indices exhibit the stylized facts reported for financial time series returns. The returns distributions of the Africa All-Share, Large, Medium and Small Company Indices were found to be... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_15592666" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper investigates if benchmark African equity indices exhibit the stylized facts reported for financial time series returns. The returns distributions of the Africa All-Share, Large, Medium and Small Company Indices were found to be leptokurtotic, had fat-tails, over time experienced volatility clustering and exhibited long memory in volatility. Both the All-Share and Large Company Indices were found to exhibit leverage effects. In contrast, positive shocks had a greater impact on future volatility for the Small Company Index which implies a reverse leverage effect. This finding could reflect a bull/bubble market for small capitalisation stocks in Africa.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/15592666" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="9c209248b7a4b54e4432780fe64bda7b" rel="nofollow" data-download="{"attachment_id":43053078,"asset_id":15592666,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43053078/download_file?st=MTc0MDU3MTkzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="34758489" href="https://independent.academia.edu/KwakuOpong">Kwaku Opong</a><script data-card-contents-for-user="34758489" type="text/json">{"id":34758489,"first_name":"Kwaku","last_name":"Opong","domain_name":"independent","page_name":"KwakuOpong","display_name":"Kwaku Opong","profile_url":"https://independent.academia.edu/KwakuOpong?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_15592666 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="15592666"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 15592666, container: ".js-paper-rank-work_15592666", }); 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The returns distributions of the Africa All-Share, Large, Medium and Small Company Indices were found to be leptokurtotic, had fat-tails, over time experienced volatility clustering and exhibited long memory in volatility. Both the All-Share and Large Company Indices were found to exhibit leverage effects. In contrast, positive shocks had a greater impact on future volatility for the Small Company Index which implies a reverse leverage effect. This finding could reflect a bull/bubble market for small capitalisation stocks in Africa.","downloadable_attachments":[{"id":43053078,"asset_id":15592666,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":34758489,"first_name":"Kwaku","last_name":"Opong","domain_name":"independent","page_name":"KwakuOpong","display_name":"Kwaku Opong","profile_url":"https://independent.academia.edu/KwakuOpong?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":45042,"name":"Long Memory","url":"https://www.academia.edu/Documents/in/Long_Memory?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true},{"id":80649,"name":"Global Finance","url":"https://www.academia.edu/Documents/in/Global_Finance?f_ri=61227","nofollow":true},{"id":88241,"name":"GARCH","url":"https://www.academia.edu/Documents/in/GARCH?f_ri=61227","nofollow":true},{"id":480921,"name":"Fat tails","url":"https://www.academia.edu/Documents/in/Fat_tails?f_ri=61227"},{"id":749302,"name":"Indexation","url":"https://www.academia.edu/Documents/in/Indexation?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_18580591" data-work_id="18580591" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/18580591/Econophysics_review_I_Empirical_facts">Econophysics review: I. Empirical facts</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">... DOI: 10.1080/14697688.2010.539248 Anirban Chakraborti a * , Ioane Muni Toke a , Marco Patriarca b c &amp;amp;amp;amp;amp;amp;amp; Frédéric Abergel a pages 991-1012. Available online: 24 Jun 2011. ...</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/18580591" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="eac0a73a8170fa29cbcbc1002f6e533b" rel="nofollow" data-download="{"attachment_id":42157915,"asset_id":18580591,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/42157915/download_file?st=MTc0MDU3MTkzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="38619200" href="https://independent.academia.edu/Fr%C3%A9d%C3%A9ricAbergel">Frédéric Abergel</a><script data-card-contents-for-user="38619200" type="text/json">{"id":38619200,"first_name":"Frédéric","last_name":"Abergel","domain_name":"independent","page_name":"FrédéricAbergel","display_name":"Frédéric Abergel","profile_url":"https://independent.academia.edu/Fr%C3%A9d%C3%A9ricAbergel?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_18580591 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="18580591"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 18580591, container: ".js-paper-rank-work_18580591", }); 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$(".js-view-count[data-work-id=18580591]").text(description); $(".js-view-count-work_18580591").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_18580591").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="18580591"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">16</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="724" rel="nofollow" href="https://www.academia.edu/Documents/in/Economics">Economics</a>, <script data-card-contents-for-ri="724" type="text/json">{"id":724,"name":"Economics","url":"https://www.academia.edu/Documents/in/Economics?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2616" rel="nofollow" href="https://www.academia.edu/Documents/in/Graph_Theory">Graph Theory</a>, <script data-card-contents-for-ri="2616" type="text/json">{"id":2616,"name":"Graph Theory","url":"https://www.academia.edu/Documents/in/Graph_Theory?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="3527" rel="nofollow" href="https://www.academia.edu/Documents/in/Computational_Finance">Computational Finance</a>, <script data-card-contents-for-ri="3527" type="text/json">{"id":3527,"name":"Computational Finance","url":"https://www.academia.edu/Documents/in/Computational_Finance?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="25221" rel="nofollow" href="https://www.academia.edu/Documents/in/Wealth_Distribution">Wealth Distribution</a><script data-card-contents-for-ri="25221" type="text/json">{"id":25221,"name":"Wealth Distribution","url":"https://www.academia.edu/Documents/in/Wealth_Distribution?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=18580591]'), work: {"id":18580591,"title":"Econophysics review: I. Empirical facts","created_at":"2015-11-18T06:11:02.266-08:00","url":"https://www.academia.edu/18580591/Econophysics_review_I_Empirical_facts?f_ri=61227","dom_id":"work_18580591","summary":"... DOI: 10.1080/14697688.2010.539248 Anirban Chakraborti a * , Ioane Muni Toke a , Marco Patriarca b c \u0026amp;amp;amp;amp;amp;amp;amp; Frédéric Abergel a pages 991-1012. Available online: 24 Jun 2011. ...","downloadable_attachments":[{"id":42157915,"asset_id":18580591,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":38619200,"first_name":"Frédéric","last_name":"Abergel","domain_name":"independent","page_name":"FrédéricAbergel","display_name":"Frédéric Abergel","profile_url":"https://independent.academia.edu/Fr%C3%A9d%C3%A9ricAbergel?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":724,"name":"Economics","url":"https://www.academia.edu/Documents/in/Economics?f_ri=61227","nofollow":true},{"id":2616,"name":"Graph Theory","url":"https://www.academia.edu/Documents/in/Graph_Theory?f_ri=61227","nofollow":true},{"id":3527,"name":"Computational Finance","url":"https://www.academia.edu/Documents/in/Computational_Finance?f_ri=61227","nofollow":true},{"id":25221,"name":"Wealth Distribution","url":"https://www.academia.edu/Documents/in/Wealth_Distribution?f_ri=61227","nofollow":true},{"id":36055,"name":"Quantitative","url":"https://www.academia.edu/Documents/in/Quantitative?f_ri=61227"},{"id":48458,"name":"High Frequency","url":"https://www.academia.edu/Documents/in/High_Frequency?f_ri=61227"},{"id":48739,"name":"Quantitative Finance","url":"https://www.academia.edu/Documents/in/Quantitative_Finance?f_ri=61227"},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences?f_ri=61227"},{"id":137277,"name":"Random Matrix Theory","url":"https://www.academia.edu/Documents/in/Random_Matrix_Theory?f_ri=61227"},{"id":219474,"name":"Empirical Study","url":"https://www.academia.edu/Documents/in/Empirical_Study?f_ri=61227"},{"id":270673,"name":"Financial Market","url":"https://www.academia.edu/Documents/in/Financial_Market?f_ri=61227"},{"id":428833,"name":"Statistical Properties","url":"https://www.academia.edu/Documents/in/Statistical_Properties?f_ri=61227"},{"id":473797,"name":"Microstructures","url":"https://www.academia.edu/Documents/in/Microstructures?f_ri=61227"},{"id":741144,"name":"Agent Based Model","url":"https://www.academia.edu/Documents/in/Agent_Based_Model?f_ri=61227"},{"id":892890,"name":"Point of View","url":"https://www.academia.edu/Documents/in/Point_of_View?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_37180223 coauthored" data-work_id="37180223" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/37180223/SVR_Wavelet_Adaptive_Model_for_Forecasting_Financial_Time_Series">SVR-Wavelet Adaptive Model for Forecasting Financial Time Series</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">There is a necessity to anticipate and identify changes in events points to a new direction in the stock exchange markets in line with the analysis of the oscillations of prices of financial assets. This need leads to argue about new... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_37180223" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">There is a necessity to anticipate and identify changes in events points to a new direction in the stock exchange markets in line with the analysis of the oscillations of prices of financial assets. This need leads to argue about new alternatives in the prediction of financial time series using machine learning methods. This paper aims to describe the development of the SVR-wavelet model, an adaptive and hybrid prediction model, which integrates wavelet models and Support Vector Regression (SVR), for prediction of financial time series, particularly applied to Foreign Exchange Market (FOREX), obtained from a public knowledge base. The method consists of using the Discrete Wavelets Transform (DWT) to decompose data from FOREX time series, that are used as SVR input variables to predict new data. The adjusted series are compared with traditional models such as ARIMA and ARFIMA Model. In Addition, statistical tests like normality and unit root are performed to prove that the series in question have non-linear distribution and also to verify the level of correlation between the periods of the series.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/37180223" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f8b5ca350d141aa4596f69d6fb3a16e0" rel="nofollow" data-download="{"attachment_id":57131577,"asset_id":37180223,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/57131577/download_file?st=MTc0MDU3MTkzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="7903320" href="https://usp-br.academia.edu/MiltonRaimundo">Milton Raimundo</a><script data-card-contents-for-user="7903320" type="text/json">{"id":7903320,"first_name":"Milton","last_name":"Raimundo","domain_name":"usp-br","page_name":"MiltonRaimundo","display_name":"Milton Raimundo","profile_url":"https://usp-br.academia.edu/MiltonRaimundo?f_ri=61227","photo":"https://0.academia-photos.com/7903320/6373001/19909857/s65_milton.raimundo.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-37180223">+1</span><div class="hidden js-additional-users-37180223"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://usp-br.academia.edu/JunOkamotoJr">Jun Okamoto Jr.</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-37180223'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-37180223').html(); 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This need leads to argue about new alternatives in the prediction of financial time series using machine learning methods. This paper aims to describe the development of the SVR-wavelet model, an adaptive and hybrid prediction model, which integrates wavelet models and Support Vector Regression (SVR), for prediction of financial time series, particularly applied to Foreign Exchange Market (FOREX), obtained from a public knowledge base. The method consists of using the Discrete Wavelets Transform (DWT) to decompose data from FOREX time series, that are used as SVR input variables to predict new data. The adjusted series are compared with traditional models such as ARIMA and ARFIMA Model. In Addition, statistical tests like normality and unit root are performed to prove that the series in question have non-linear distribution and also to verify the level of correlation between the periods of the series.","downloadable_attachments":[{"id":57131577,"asset_id":37180223,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":7903320,"first_name":"Milton","last_name":"Raimundo","domain_name":"usp-br","page_name":"MiltonRaimundo","display_name":"Milton Raimundo","profile_url":"https://usp-br.academia.edu/MiltonRaimundo?f_ri=61227","photo":"https://0.academia-photos.com/7903320/6373001/19909857/s65_milton.raimundo.png"},{"id":52725198,"first_name":"Jun","last_name":"Okamoto Jr.","domain_name":"usp-br","page_name":"JunOkamotoJr","display_name":"Jun Okamoto Jr.","profile_url":"https://usp-br.academia.edu/JunOkamotoJr?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=61227","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_1507287" data-work_id="1507287" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/1507287/Fractional_calculus_and_continuous_time_finance_II_the_waiting_time_distribution">Fractional calculus and continuous-time finance II: the waiting-time distribution</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We complement the theory of tick-by-tick dynamics of financial markets based on a continuous-time random walk (CTRW) model recently proposed by Scalas et al. (Physica A 284 (2000) 376), and we point out its consistency with the behaviour... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_1507287" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We complement the theory of tick-by-tick dynamics of financial markets based on a continuous-time random walk (CTRW) model recently proposed by Scalas et al. (Physica A 284 (2000) 376), and we point out its consistency with the behaviour observed in the waiting-time distribution for BUND future prices traded at LIFFE, London.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/1507287" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ddab4b79feed7e3e91d2cf6b84fa7b68" rel="nofollow" data-download="{"attachment_id":50945998,"asset_id":1507287,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50945998/download_file?st=MTc0MDU3MTkzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1502111" href="https://unige-it.academia.edu/MarcoRaberto">Marco Raberto</a><script data-card-contents-for-user="1502111" type="text/json">{"id":1502111,"first_name":"Marco","last_name":"Raberto","domain_name":"unige-it","page_name":"MarcoRaberto","display_name":"Marco Raberto","profile_url":"https://unige-it.academia.edu/MarcoRaberto?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_1507287 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="1507287"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 1507287, container: ".js-paper-rank-work_1507287", }); 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(Physica A 284 (2000) 376), and we point out its consistency with the behaviour observed in the waiting-time distribution for BUND future prices traded at LIFFE, London.","downloadable_attachments":[{"id":50945998,"asset_id":1507287,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1502111,"first_name":"Marco","last_name":"Raberto","domain_name":"unige-it","page_name":"MarcoRaberto","display_name":"Marco Raberto","profile_url":"https://unige-it.academia.edu/MarcoRaberto?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":47,"name":"Finance","url":"https://www.academia.edu/Documents/in/Finance?f_ri=61227","nofollow":true},{"id":318,"name":"Mathematical Physics","url":"https://www.academia.edu/Documents/in/Mathematical_Physics?f_ri=61227","nofollow":true},{"id":347,"name":"Stochastic Process","url":"https://www.academia.edu/Documents/in/Stochastic_Process?f_ri=61227","nofollow":true},{"id":518,"name":"Quantum Physics","url":"https://www.academia.edu/Documents/in/Quantum_Physics?f_ri=61227","nofollow":true},{"id":43131,"name":"Stochastic processes","url":"https://www.academia.edu/Documents/in/Stochastic_processes?f_ri=61227"},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227"},{"id":78086,"name":"Random Walk","url":"https://www.academia.edu/Documents/in/Random_Walk?f_ri=61227"},{"id":127241,"name":"Duration","url":"https://www.academia.edu/Documents/in/Duration?f_ri=61227"},{"id":213709,"name":"Mathematical","url":"https://www.academia.edu/Documents/in/Mathematical?f_ri=61227"},{"id":270673,"name":"Financial Market","url":"https://www.academia.edu/Documents/in/Financial_Market?f_ri=61227"},{"id":287495,"name":"Waiting Time distribution","url":"https://www.academia.edu/Documents/in/Waiting_Time_distribution?f_ri=61227"},{"id":327041,"name":"Fractional Calculus","url":"https://www.academia.edu/Documents/in/Fractional_Calculus?f_ri=61227"},{"id":588227,"name":"PROBABILITY DENSITY","url":"https://www.academia.edu/Documents/in/PROBABILITY_DENSITY?f_ri=61227"},{"id":663624,"name":"Statistical Finance","url":"https://www.academia.edu/Documents/in/Statistical_Finance?f_ri=61227"},{"id":991097,"name":"Continuous Time Systems","url":"https://www.academia.edu/Documents/in/Continuous_Time_Systems?f_ri=61227"},{"id":1158716,"name":"Waiting Time","url":"https://www.academia.edu/Documents/in/Waiting_Time?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_29534423" data-work_id="29534423" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/29534423/Optimal_hedging_using_cointegration">Optimal hedging using cointegration</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/29534423" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d790371a289230ef59e3c9120fbf5bdf" rel="nofollow" data-download="{"attachment_id":49977263,"asset_id":29534423,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" 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u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We address the general problem of how to quantify the kinematics of time series with stationary first moments but having non stationary multifractal long-range correlated second moments. We show that a Markov process is sufficient to... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_9605398" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We address the general problem of how to quantify the kinematics of time series with stationary first moments but having non stationary multifractal long-range correlated second moments. We show that a Markov process is sufficient to model important aspects of the multifractality observed in financial time series and propose a kinematic model of price fluctuations. We test the proposed model by analyzing index closing prices of the New York Stock Exchange and the DEM/USD tick-by-tick exchange rates obtained from Reuters EFX. We show that the model captures the characteristic features observed in actual financial time series, including volatility clustering, time scaling and fat tails in the probability density functions, power-law behavior of volatility correlations and, most importantly, the observed nonuniversal multifractal singularity spectrum. Motivated by our finding of strong agreement between the model and the data, we argue that at least two independent stochastic Gaussian variables are required to adequately model price fluctuations. r</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/9605398" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d52db87633385636094f34fc33971aca" rel="nofollow" data-download="{"attachment_id":47708215,"asset_id":9605398,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/47708215/download_file?st=MTc0MDU3MTkzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="22811599" href="https://ufal.academia.edu/IramGl%C3%A9ria">Iram Gléria</a><script data-card-contents-for-user="22811599" type="text/json">{"id":22811599,"first_name":"Iram","last_name":"Gléria","domain_name":"ufal","page_name":"IramGléria","display_name":"Iram Gléria","profile_url":"https://ufal.academia.edu/IramGl%C3%A9ria?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_9605398 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="9605398"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 9605398, container: ".js-paper-rank-work_9605398", }); 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We show that a Markov process is sufficient to model important aspects of the multifractality observed in financial time series and propose a kinematic model of price fluctuations. We test the proposed model by analyzing index closing prices of the New York Stock Exchange and the DEM/USD tick-by-tick exchange rates obtained from Reuters EFX. We show that the model captures the characteristic features observed in actual financial time series, including volatility clustering, time scaling and fat tails in the probability density functions, power-law behavior of volatility correlations and, most importantly, the observed nonuniversal multifractal singularity spectrum. Motivated by our finding of strong agreement between the model and the data, we argue that at least two independent stochastic Gaussian variables are required to adequately model price fluctuations. r","downloadable_attachments":[{"id":47708215,"asset_id":9605398,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":22811599,"first_name":"Iram","last_name":"Gléria","domain_name":"ufal","page_name":"IramGléria","display_name":"Iram Gléria","profile_url":"https://ufal.academia.edu/IramGl%C3%A9ria?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":318,"name":"Mathematical Physics","url":"https://www.academia.edu/Documents/in/Mathematical_Physics?f_ri=61227","nofollow":true},{"id":518,"name":"Quantum Physics","url":"https://www.academia.edu/Documents/in/Quantum_Physics?f_ri=61227","nofollow":true},{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true},{"id":30485,"name":"Time series analysis","url":"https://www.academia.edu/Documents/in/Time_series_analysis?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227"},{"id":113890,"name":"Power Law","url":"https://www.academia.edu/Documents/in/Power_Law?f_ri=61227"},{"id":228986,"name":"Exchange rate","url":"https://www.academia.edu/Documents/in/Exchange_rate?f_ri=61227"},{"id":741671,"name":"Markov Process","url":"https://www.academia.edu/Documents/in/Markov_Process?f_ri=61227"},{"id":749302,"name":"Indexation","url":"https://www.academia.edu/Documents/in/Indexation?f_ri=61227"},{"id":871028,"name":"Singular-spectrum Analysis","url":"https://www.academia.edu/Documents/in/Singular-spectrum_Analysis?f_ri=61227"},{"id":872399,"name":"Probability Density Function","url":"https://www.academia.edu/Documents/in/Probability_Density_Function?f_ri=61227"},{"id":899955,"name":"New York Stock Exchange","url":"https://www.academia.edu/Documents/in/New_York_Stock_Exchange?f_ri=61227"},{"id":2050770,"name":"Markov model","url":"https://www.academia.edu/Documents/in/Markov_model?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_18147375" data-work_id="18147375" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/18147375/Chapter_23_Heterogeneous_Agent_Models_in_Economics_and_Finance">Chapter 23 Heterogeneous Agent Models in Economics and Finance</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This chapter surveys work on dynamic heterogeneous agent models (HAMs) in economics and finance. Emphasis is given to simple models that, at least to some extent, are tractable by analytic methods in combination with computational tools.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_18147375" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This chapter surveys work on dynamic heterogeneous agent models (HAMs) in economics and finance. Emphasis is given to simple models that, at least to some extent, are tractable by analytic methods in combination with computational tools. Most of these models are behavioral models with boundedly rational agents using different heuristics or rule of thumb strategies that may not be perfect,</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/18147375" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="32e47dc23d2c234ed4822f5d60bf2f9e" rel="nofollow" data-download="{"attachment_id":42189854,"asset_id":18147375,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/42189854/download_file?st=MTc0MDU3MTkzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="38111062" href="https://independent.academia.edu/CarsHommes">Cars Hommes</a><script data-card-contents-for-user="38111062" type="text/json">{"id":38111062,"first_name":"Cars","last_name":"Hommes","domain_name":"independent","page_name":"CarsHommes","display_name":"Cars Hommes","profile_url":"https://independent.academia.edu/CarsHommes?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_18147375 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="18147375"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 18147375, container: ".js-paper-rank-work_18147375", }); 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href="https://www.academia.edu/61790070/Financial_time_series_and_neural_networks_in_a_minority_game_context">Financial time series and neural networks in a minority game context</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_61790070" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and 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class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/48594855/Application_of_support_vector_machines_in_financial_time_series_forecasting">Application of support vector machines in financial time series forecasting</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper deals with the application of a novel neural network technique, support vector machine (SVM), in ÿnancial time series forecasting. The objective of this paper is to examine the feasibility of SVM in ÿnancial time series... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_48594855" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper deals with the application of a novel neural network technique, support vector machine (SVM), in ÿnancial time series forecasting. The objective of this paper is to examine the feasibility of SVM in ÿnancial time series forecasting by comparing it with a multi-layer back-propagation (BP) neural network. Five real futures contracts that are collated from the Chicago Mercantile Market are used as the data sets. The experiment shows that SVM outperforms the BP neural network based on the criteria of normalized mean square error (NMSE), mean absolute error (MAE), directional symmetry (DS) and weighted directional symmetry (WDS). Since there is no structured way to choose the free parameters of SVMs, the variability in performance with respect to the free parameters is investigated in this study. Analysis of the experimental results proved that it is advantageous to apply SVMs to forecast ÿnancial time series.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/48594855" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f6e6ed9bc1366c5e3b32f0c8a732f3cf" rel="nofollow" data-download="{"attachment_id":67125836,"asset_id":48594855,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/67125836/download_file?st=MTc0MDU3MTkzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="154127646" href="https://nus.academia.edu/FrancisTay">Francis Tay</a><script data-card-contents-for-user="154127646" type="text/json">{"id":154127646,"first_name":"Francis","last_name":"Tay","domain_name":"nus","page_name":"FrancisTay","display_name":"Francis Tay","profile_url":"https://nus.academia.edu/FrancisTay?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_48594855 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="48594855"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 48594855, container: ".js-paper-rank-work_48594855", }); 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The objective of this paper is to examine the feasibility of SVM in ÿnancial time series forecasting by comparing it with a multi-layer back-propagation (BP) neural network. Five real futures contracts that are collated from the Chicago Mercantile Market are used as the data sets. The experiment shows that SVM outperforms the BP neural network based on the criteria of normalized mean square error (NMSE), mean absolute error (MAE), directional symmetry (DS) and weighted directional symmetry (WDS). Since there is no structured way to choose the free parameters of SVMs, the variability in performance with respect to the free parameters is investigated in this study. Analysis of the experimental results proved that it is advantageous to apply SVMs to forecast ÿnancial time series.","downloadable_attachments":[{"id":67125836,"asset_id":48594855,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":154127646,"first_name":"Francis","last_name":"Tay","domain_name":"nus","page_name":"FrancisTay","display_name":"Francis Tay","profile_url":"https://nus.academia.edu/FrancisTay?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":39,"name":"Marketing","url":"https://www.academia.edu/Documents/in/Marketing?f_ri=61227","nofollow":true},{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true},{"id":5187,"name":"Statistical Analysis","url":"https://www.academia.edu/Documents/in/Statistical_Analysis?f_ri=61227","nofollow":true},{"id":5750,"name":"Back Propagation","url":"https://www.academia.edu/Documents/in/Back_Propagation?f_ri=61227","nofollow":true},{"id":7461,"name":"Financial Literacy","url":"https://www.academia.edu/Documents/in/Financial_Literacy?f_ri=61227"},{"id":10408,"name":"Support Vector Machines","url":"https://www.academia.edu/Documents/in/Support_Vector_Machines?f_ri=61227"},{"id":26066,"name":"Neural Network","url":"https://www.academia.edu/Documents/in/Neural_Network?f_ri=61227"},{"id":36224,"name":"Economic Forecasting","url":"https://www.academia.edu/Documents/in/Economic_Forecasting?f_ri=61227"},{"id":54284,"name":"Generalization","url":"https://www.academia.edu/Documents/in/Generalization?f_ri=61227"},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227"},{"id":69116,"name":"OMEGA","url":"https://www.academia.edu/Documents/in/OMEGA?f_ri=61227"},{"id":73149,"name":"Business and Management","url":"https://www.academia.edu/Documents/in/Business_and_Management?f_ri=61227"},{"id":191289,"name":"Support vector machine","url":"https://www.academia.edu/Documents/in/Support_vector_machine?f_ri=61227"},{"id":327017,"name":"Credit Cards","url":"https://www.academia.edu/Documents/in/Credit_Cards?f_ri=61227"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network?f_ri=61227"},{"id":1453177,"name":"Normalized Mean Square Error","url":"https://www.academia.edu/Documents/in/Normalized_Mean_Square_Error?f_ri=61227"},{"id":1837730,"name":"Neural Network Model","url":"https://www.academia.edu/Documents/in/Neural_Network_Model?f_ri=61227"},{"id":2240993,"name":"Backpropagation Algorithm","url":"https://www.academia.edu/Documents/in/Backpropagation_Algorithm?f_ri=61227"},{"id":2925591,"name":"Mean Absolute Error","url":"https://www.academia.edu/Documents/in/Mean_Absolute_Error?f_ri=61227"},{"id":3259467,"name":"Structural Risk Minimization","url":"https://www.academia.edu/Documents/in/Structural_Risk_Minimization?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_37180219 coauthored" data-work_id="37180219" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/37180219/Application_of_Hurst_Exponent_H_and_the_RS_Analysis_in_the_Classification_of_FOREX_Securities_DOC_pdf">Application of Hurst Exponent H and the RS Analysis in the Classification of FOREX Securities (DOC).pdf</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper presents the relationship between the Hurst Exponent (H) and the Rescaled Range Analysis (R/S) in the classification of Foreign Exchange Market (FOREX) time series by the supposition of the existence of a Fractal Market in an... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_37180219" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper presents the relationship between the Hurst Exponent (H) and the Rescaled Range Analysis (R/S) in the classification of Foreign Exchange Market (FOREX) time series by the supposition of the existence of a Fractal Market in an alternative to the traditional theory of Capital Markets. In such a way, the Hurst Exponent is a metric capable of providing information on correlation and persistence in a time series. Many systems can be described by self-similar fractals as Fractional Brownian Motion, which are well characterized by this statistic.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/37180219" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f6e553030219eac077c4de7030369471" rel="nofollow" data-download="{"attachment_id":57131571,"asset_id":37180219,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/57131571/download_file?st=MTc0MDU3MTkzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="7903320" href="https://usp-br.academia.edu/MiltonRaimundo">Milton Raimundo</a><script data-card-contents-for-user="7903320" type="text/json">{"id":7903320,"first_name":"Milton","last_name":"Raimundo","domain_name":"usp-br","page_name":"MiltonRaimundo","display_name":"Milton Raimundo","profile_url":"https://usp-br.academia.edu/MiltonRaimundo?f_ri=61227","photo":"https://0.academia-photos.com/7903320/6373001/19909857/s65_milton.raimundo.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-37180219">+1</span><div class="hidden js-additional-users-37180219"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://usp-br.academia.edu/JunOkamotoJr">Jun Okamoto Jr.</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-37180219'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-37180219').html(); 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In such a way, the Hurst Exponent is a metric capable of providing information on correlation and persistence in a time series. Many systems can be described by self-similar fractals as Fractional Brownian Motion, which are well characterized by this statistic.","downloadable_attachments":[{"id":57131571,"asset_id":37180219,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":7903320,"first_name":"Milton","last_name":"Raimundo","domain_name":"usp-br","page_name":"MiltonRaimundo","display_name":"Milton Raimundo","profile_url":"https://usp-br.academia.edu/MiltonRaimundo?f_ri=61227","photo":"https://0.academia-photos.com/7903320/6373001/19909857/s65_milton.raimundo.png"},{"id":52725198,"first_name":"Jun","last_name":"Okamoto Jr.","domain_name":"usp-br","page_name":"JunOkamotoJr","display_name":"Jun Okamoto Jr.","profile_url":"https://usp-br.academia.edu/JunOkamotoJr?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=61227","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_3173342 coauthored" data-work_id="3173342" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/3173342/Improving_financial_time_series_prediction_using_exogenous_series_and_neural_networks_committees">Improving financial time series prediction using exogenous series and neural networks committees</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Time series forecasting is useful in many researches areas. The use of models that provide a reliable prediction in financial time series may bring valuable profits for the investors. This paper proposes a methodology based on information... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_3173342" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Time series forecasting is useful in many researches areas. The use of models that provide a reliable prediction in financial time series may bring valuable profits for the investors. This paper proposes a methodology based on information obtained from exogenous series used in combination with neural networks to predict stock series. The best trained neural networks were used in combination to improve the prediction capacity of a single networks. To evaluate the proposed prediction models, some known metrics were applied. Moreover, we also proposed one new metric called Prediction in Direction and Accuracy (PDA), which benefits models with great performance in prediction accuracy and trend. Addictionally, there was used an evolutionary algorithm to choose the best trained models that maximize PDA. Experiments with two of the most important Brazilian companies stock quotes have shown the usefulness of the proposed prediction system to generate profits in investments.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/3173342" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d81eaaf6f56863395ffa24741cdda080" rel="nofollow" data-download="{"attachment_id":31066552,"asset_id":3173342,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/31066552/download_file?st=MTc0MDU3MTkzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="3624077" href="https://ufpe.academia.edu/IngRenTsang">Ing Ren Tsang</a><script data-card-contents-for-user="3624077" type="text/json">{"id":3624077,"first_name":"Ing Ren","last_name":"Tsang","domain_name":"ufpe","page_name":"IngRenTsang","display_name":"Ing Ren Tsang","profile_url":"https://ufpe.academia.edu/IngRenTsang?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-3173342">+1</span><div class="hidden js-additional-users-3173342"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/GeorgeDarmitonCunhaCavalcanti">George Darmiton Cunha Cavalcanti</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-3173342'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-3173342').html(); 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The use of models that provide a reliable prediction in financial time series may bring valuable profits for the investors. This paper proposes a methodology based on information obtained from exogenous series used in combination with neural networks to predict stock series. The best trained neural networks were used in combination to improve the prediction capacity of a single networks. To evaluate the proposed prediction models, some known metrics were applied. Moreover, we also proposed one new metric called Prediction in Direction and Accuracy (PDA), which benefits models with great performance in prediction accuracy and trend. Addictionally, there was used an evolutionary algorithm to choose the best trained models that maximize PDA. Experiments with two of the most important Brazilian companies stock quotes have shown the usefulness of the proposed prediction system to generate profits in investments.","downloadable_attachments":[{"id":31066552,"asset_id":3173342,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":3624077,"first_name":"Ing Ren","last_name":"Tsang","domain_name":"ufpe","page_name":"IngRenTsang","display_name":"Ing Ren Tsang","profile_url":"https://ufpe.academia.edu/IngRenTsang?f_ri=61227","photo":"/images/s65_no_pic.png"},{"id":36461988,"first_name":"George Darmiton Cunha","last_name":"Cavalcanti","domain_name":"independent","page_name":"GeorgeDarmitonCunhaCavalcanti","display_name":"George Darmiton Cunha Cavalcanti","profile_url":"https://independent.academia.edu/GeorgeDarmitonCunhaCavalcanti?f_ri=61227","photo":"https://0.academia-photos.com/36461988/18529257/18496481/s65_george_darmiton_cunha.cavalcanti.png"}],"research_interests":[{"id":3523,"name":"Evolutionary Computation","url":"https://www.academia.edu/Documents/in/Evolutionary_Computation?f_ri=61227","nofollow":true},{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true},{"id":26066,"name":"Neural Network","url":"https://www.academia.edu/Documents/in/Neural_Network?f_ri=61227","nofollow":true},{"id":27360,"name":"Databases","url":"https://www.academia.edu/Documents/in/Databases?f_ri=61227","nofollow":true},{"id":30485,"name":"Time series analysis","url":"https://www.academia.edu/Documents/in/Time_series_analysis?f_ri=61227"},{"id":54123,"name":"Artificial Neural Networks","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Networks?f_ri=61227"},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227"},{"id":78434,"name":"Investment","url":"https://www.academia.edu/Documents/in/Investment?f_ri=61227"},{"id":96446,"name":"Measurement","url":"https://www.academia.edu/Documents/in/Measurement?f_ri=61227"},{"id":121035,"name":"Profitability","url":"https://www.academia.edu/Documents/in/Profitability?f_ri=61227"},{"id":165689,"name":"Profit","url":"https://www.academia.edu/Documents/in/Profit?f_ri=61227"},{"id":204472,"name":"Predictive models","url":"https://www.academia.edu/Documents/in/Predictive_models?f_ri=61227"},{"id":224767,"name":"Prediction Model","url":"https://www.academia.edu/Documents/in/Prediction_Model?f_ri=61227"},{"id":265625,"name":"Evolutionary Algorithm","url":"https://www.academia.edu/Documents/in/Evolutionary_Algorithm?f_ri=61227"},{"id":291387,"name":"Mathematical Model","url":"https://www.academia.edu/Documents/in/Mathematical_Model?f_ri=61227"},{"id":366369,"name":"Time Series Forecasting","url":"https://www.academia.edu/Documents/in/Time_Series_Forecasting?f_ri=61227"},{"id":1671808,"name":"Prediction Accuracy","url":"https://www.academia.edu/Documents/in/Prediction_Accuracy?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_25758674" data-work_id="25758674" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/25758674/The_Vietnams_Transition_Economy_and_Its_Fledgling_Financial_Markets_1986_2003">The Vietnam's Transition Economy and Its Fledgling Financial Markets: 1986-2003</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Abstract: In this paper, we analyze the context of Vietnam&#x27;s economic standings in the reform period. The first section embarks on most remarkable factors, which promote the development of financial markets are:(i) Doi Moi policies... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_25758674" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Abstract: In this paper, we analyze the context of Vietnam&#x27;s economic standings in the reform period. The first section embarks on most remarkable factors, which promote the development of financial markets are:(i) Doi Moi policies in 1986 unleash&#x27;productive powers.&#x27;Real GDP growth, and key economic indicators improve. The economy truly departs from the old-style command economy;(ii) FDI component is present in the economy as sine qua non; a crucial growth engine, forming part of the financial markets, planting the&#x27;seeds&#x27; ...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/25758674" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7d3724eda6b91ba9725285d29468353b" rel="nofollow" data-download="{"attachment_id":46115174,"asset_id":25758674,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/46115174/download_file?st=MTc0MDU3MTkzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="49368646" href="https://independent.academia.edu/QuanHoangVuong">Quan-Hoang Vuong</a><script data-card-contents-for-user="49368646" type="text/json">{"id":49368646,"first_name":"Quan-Hoang","last_name":"Vuong","domain_name":"independent","page_name":"QuanHoangVuong","display_name":"Quan-Hoang Vuong","profile_url":"https://independent.academia.edu/QuanHoangVuong?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_25758674 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="25758674"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 25758674, container: ".js-paper-rank-work_25758674", }); 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The first section embarks on most remarkable factors, which promote the development of financial markets are:(i) Doi Moi policies in 1986 unleash\u0026#x27;productive powers.\u0026#x27;Real GDP growth, and key economic indicators improve. The economy truly departs from the old-style command economy;(ii) FDI component is present in the economy as sine qua non; a crucial growth engine, forming part of the financial markets, planting the\u0026#x27;seeds\u0026#x27; ...","downloadable_attachments":[{"id":46115174,"asset_id":25758674,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":49368646,"first_name":"Quan-Hoang","last_name":"Vuong","domain_name":"independent","page_name":"QuanHoangVuong","display_name":"Quan-Hoang Vuong","profile_url":"https://independent.academia.edu/QuanHoangVuong?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":16655,"name":"Vietnam","url":"https://www.academia.edu/Documents/in/Vietnam?f_ri=61227","nofollow":true},{"id":29156,"name":"Stock Market","url":"https://www.academia.edu/Documents/in/Stock_Market?f_ri=61227","nofollow":true},{"id":55704,"name":"Transition","url":"https://www.academia.edu/Documents/in/Transition?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true},{"id":270673,"name":"Financial Market","url":"https://www.academia.edu/Documents/in/Financial_Market?f_ri=61227"},{"id":393134,"name":"Empirical evidence","url":"https://www.academia.edu/Documents/in/Empirical_evidence?f_ri=61227"},{"id":466484,"name":"Private Sector","url":"https://www.academia.edu/Documents/in/Private_Sector?f_ri=61227"},{"id":729303,"name":"Financial Economy","url":"https://www.academia.edu/Documents/in/Financial_Economy?f_ri=61227"},{"id":873221,"name":"Transition Economy","url":"https://www.academia.edu/Documents/in/Transition_Economy?f_ri=61227"},{"id":1126322,"name":"Private Investment","url":"https://www.academia.edu/Documents/in/Private_Investment?f_ri=61227"},{"id":1367101,"name":"GDP Growth","url":"https://www.academia.edu/Documents/in/GDP_Growth?f_ri=61227"},{"id":1770708,"name":"Economic Evolution","url":"https://www.academia.edu/Documents/in/Economic_Evolution?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_15162083" data-work_id="15162083" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/15162083/Bayesian_estimation_of_the_Gaussian_mixture_GARCH_model">Bayesian estimation of the Gaussian mixture GARCH model</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this paper, we perform Bayesian inference and prediction for a GARCH model where the innovations are assumed to follow a mixture of two Gaussian distributions. This GARCH model can capture the patterns usually exhibited by many... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_15162083" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this paper, we perform Bayesian inference and prediction for a GARCH model where the innovations are assumed to follow a mixture of two Gaussian distributions. This GARCH model can capture the patterns usually exhibited by many financial time series such as volatility clustering, large kurtosis and extreme observations. A Griddy-Gibbs sampler implementation is proposed for parameter estimation and volatility prediction. The method is illustrated using the Swiss Market Index. . We wish to thank Michael P. Wiper for useful comments and suggestions. We acknowledge financial support by project SEJ2004-03303.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/15162083" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="6baa981c73781040ec2aa97767cb9ed2" rel="nofollow" data-download="{"attachment_id":43517597,"asset_id":15162083,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43517597/download_file?st=MTc0MDU3MTkzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="34209553" href="https://independent.academia.edu/GaleanoPedro">Pedro Galeano</a><script data-card-contents-for-user="34209553" type="text/json">{"id":34209553,"first_name":"Pedro","last_name":"Galeano","domain_name":"independent","page_name":"GaleanoPedro","display_name":"Pedro Galeano","profile_url":"https://independent.academia.edu/GaleanoPedro?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_15162083 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="15162083"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 15162083, container: ".js-paper-rank-work_15162083", }); 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$(".js-view-count[data-work-id=15162083]").text(description); $(".js-view-count-work_15162083").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_15162083").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="15162083"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">12</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="747" rel="nofollow" href="https://www.academia.edu/Documents/in/Econometrics">Econometrics</a>, <script data-card-contents-for-ri="747" type="text/json">{"id":747,"name":"Econometrics","url":"https://www.academia.edu/Documents/in/Econometrics?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="892" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistics">Statistics</a>, <script data-card-contents-for-ri="892" type="text/json">{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4456" rel="nofollow" href="https://www.academia.edu/Documents/in/Time_Series">Time Series</a>, <script data-card-contents-for-ri="4456" type="text/json">{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="39920" rel="nofollow" href="https://www.academia.edu/Documents/in/Parameter_estimation">Parameter estimation</a><script data-card-contents-for-ri="39920" type="text/json">{"id":39920,"name":"Parameter estimation","url":"https://www.academia.edu/Documents/in/Parameter_estimation?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=15162083]'), work: {"id":15162083,"title":"Bayesian estimation of the Gaussian mixture GARCH model","created_at":"2015-08-25T01:16:55.626-07:00","url":"https://www.academia.edu/15162083/Bayesian_estimation_of_the_Gaussian_mixture_GARCH_model?f_ri=61227","dom_id":"work_15162083","summary":"In this paper, we perform Bayesian inference and prediction for a GARCH model where the innovations are assumed to follow a mixture of two Gaussian distributions. This GARCH model can capture the patterns usually exhibited by many financial time series such as volatility clustering, large kurtosis and extreme observations. A Griddy-Gibbs sampler implementation is proposed for parameter estimation and volatility prediction. The method is illustrated using the Swiss Market Index. . We wish to thank Michael P. Wiper for useful comments and suggestions. We acknowledge financial support by project SEJ2004-03303.","downloadable_attachments":[{"id":43517597,"asset_id":15162083,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":34209553,"first_name":"Pedro","last_name":"Galeano","domain_name":"independent","page_name":"GaleanoPedro","display_name":"Pedro Galeano","profile_url":"https://independent.academia.edu/GaleanoPedro?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":747,"name":"Econometrics","url":"https://www.academia.edu/Documents/in/Econometrics?f_ri=61227","nofollow":true},{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=61227","nofollow":true},{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true},{"id":39920,"name":"Parameter estimation","url":"https://www.academia.edu/Documents/in/Parameter_estimation?f_ri=61227","nofollow":true},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=61227"},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227"},{"id":389829,"name":"Gaussian distribution","url":"https://www.academia.edu/Documents/in/Gaussian_distribution?f_ri=61227"},{"id":423482,"name":"Mixture Model","url":"https://www.academia.edu/Documents/in/Mixture_Model?f_ri=61227"},{"id":512859,"name":"Gaussian Mixture","url":"https://www.academia.edu/Documents/in/Gaussian_Mixture?f_ri=61227"},{"id":749302,"name":"Indexation","url":"https://www.academia.edu/Documents/in/Indexation?f_ri=61227"},{"id":1496485,"name":"Computational Statistics and Data Analysis","url":"https://www.academia.edu/Documents/in/Computational_Statistics_and_Data_Analysis?f_ri=61227"},{"id":1951089,"name":"Bayesian Estimator","url":"https://www.academia.edu/Documents/in/Bayesian_Estimator?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_15162102" data-work_id="15162102" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/15162102/Modeling_financial_time_series_with_the_skew_slash_distribution">Modeling financial time series with the skew slash distribution</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Financial returns often present moderate skewness and high kurtosis. As a consequence, it is natural to look for a model that is exible enough to capture these characteristics. The proposal is to undertake inference for a generalized... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_15162102" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Financial returns often present moderate skewness and high kurtosis. As a consequence, it is natural to look for a model that is exible enough to capture these characteristics. The proposal is to undertake inference for a generalized autoregressive conditional heteroskedastic (GARCH) model, where the innovations are assumed to follow a skew slash distribution. Both classical and Bayesian inference are carried</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/15162102" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7afa26846c2be8046cb9fe190e88e9d9" rel="nofollow" data-download="{"attachment_id":38563560,"asset_id":15162102,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/38563560/download_file?st=MTc0MDU3MTkzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="34209553" href="https://independent.academia.edu/GaleanoPedro">Pedro Galeano</a><script data-card-contents-for-user="34209553" type="text/json">{"id":34209553,"first_name":"Pedro","last_name":"Galeano","domain_name":"independent","page_name":"GaleanoPedro","display_name":"Pedro Galeano","profile_url":"https://independent.academia.edu/GaleanoPedro?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_15162102 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="15162102"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 15162102, container: ".js-paper-rank-work_15162102", }); 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$(".js-view-count[data-work-id=15162102]").text(description); $(".js-view-count-work_15162102").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_15162102").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="15162102"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="51529" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Inference">Bayesian Inference</a>, <script data-card-contents-for-ri="51529" type="text/json">{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="61227" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_time_series">Financial time series</a><script data-card-contents-for-ri="61227" type="text/json">{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=15162102]'), work: {"id":15162102,"title":"Modeling financial time series with the skew slash distribution","created_at":"2015-08-25T01:16:58.118-07:00","url":"https://www.academia.edu/15162102/Modeling_financial_time_series_with_the_skew_slash_distribution?f_ri=61227","dom_id":"work_15162102","summary":"Financial returns often present moderate skewness and high kurtosis. As a consequence, it is natural to look for a model that is exible enough to capture these characteristics. The proposal is to undertake inference for a generalized autoregressive conditional heteroskedastic (GARCH) model, where the innovations are assumed to follow a skew slash distribution. Both classical and Bayesian inference are carried","downloadable_attachments":[{"id":38563560,"asset_id":15162102,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":34209553,"first_name":"Pedro","last_name":"Galeano","domain_name":"independent","page_name":"GaleanoPedro","display_name":"Pedro Galeano","profile_url":"https://independent.academia.edu/GaleanoPedro?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_19215330" data-work_id="19215330" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/19215330/Continuous_cascade_models_for_asset_returns">Continuous cascade models for asset returns</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this paper, we make a short overview of continuous cascade models recently introduced to model asset return fluctuations. We show that these models account in a very parcimonious manner for most of "stylized facts" of financial time... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_19215330" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this paper, we make a short overview of continuous cascade models recently introduced to model asset return fluctuations. We show that these models account in a very parcimonious manner for most of "stylized facts" of financial time series. We review in more details the simplest of such models namely the log-normal Multifractal Random Walk. It can simply be considered as a stochastic volatility model where the (log-) volatility memory has a peculiar "logarithmic" shape. This model possesses some appealing stability properties with respect to time aggregation. We describe how one can estimate it using a GMM method and we present some applications to volatility and VaR forecasting. * Electronic address: <a href="mailto:emmanuel.bacry@polytechnique.fr" rel="nofollow">emmanuel.bacry@polytechnique.fr</a> † Electronic address: <a href="mailto:alexey@cmapx.polytechnique.fr" rel="nofollow">alexey@cmapx.polytechnique.fr</a> ‡ Electronic address: <a href="mailto:muzy@univ-corse.fr" rel="nofollow">muzy@univ-corse.fr</a></div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/19215330" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="651297ca672668315c6a50da04e5b0a2" rel="nofollow" data-download="{"attachment_id":40495606,"asset_id":19215330,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/40495606/download_file?st=MTc0MDU3MTkzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="39443311" href="https://independent.academia.edu/Jeanfran%C3%A7oisMuzy">Jean-françois Muzy</a><script data-card-contents-for-user="39443311" type="text/json">{"id":39443311,"first_name":"Jean-françois","last_name":"Muzy","domain_name":"independent","page_name":"JeanfrançoisMuzy","display_name":"Jean-françois Muzy","profile_url":"https://independent.academia.edu/Jeanfran%C3%A7oisMuzy?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_19215330 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="19215330"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 19215330, container: ".js-paper-rank-work_19215330", }); });</script></li><li class="js-percentile-work_19215330 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 19215330; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_19215330"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_19215330 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="19215330"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 19215330; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=19215330]").text(description); $(".js-view-count-work_19215330").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_19215330").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="19215330"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="6208" rel="nofollow" href="https://www.academia.edu/Documents/in/Economic_Theory">Economic Theory</a>, <script data-card-contents-for-ri="6208" type="text/json">{"id":6208,"name":"Economic Theory","url":"https://www.academia.edu/Documents/in/Economic_Theory?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="27659" rel="nofollow" href="https://www.academia.edu/Documents/in/Applied_Economics">Applied Economics</a>, <script data-card-contents-for-ri="27659" type="text/json">{"id":27659,"name":"Applied Economics","url":"https://www.academia.edu/Documents/in/Applied_Economics?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="61227" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_time_series">Financial time series</a>, <script data-card-contents-for-ri="61227" type="text/json">{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="78086" rel="nofollow" href="https://www.academia.edu/Documents/in/Random_Walk">Random Walk</a><script data-card-contents-for-ri="78086" type="text/json">{"id":78086,"name":"Random Walk","url":"https://www.academia.edu/Documents/in/Random_Walk?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=19215330]'), work: {"id":19215330,"title":"Continuous cascade models for asset returns","created_at":"2015-11-30T00:56:11.185-08:00","url":"https://www.academia.edu/19215330/Continuous_cascade_models_for_asset_returns?f_ri=61227","dom_id":"work_19215330","summary":"In this paper, we make a short overview of continuous cascade models recently introduced to model asset return fluctuations. We show that these models account in a very parcimonious manner for most of \"stylized facts\" of financial time series. We review in more details the simplest of such models namely the log-normal Multifractal Random Walk. It can simply be considered as a stochastic volatility model where the (log-) volatility memory has a peculiar \"logarithmic\" shape. This model possesses some appealing stability properties with respect to time aggregation. We describe how one can estimate it using a GMM method and we present some applications to volatility and VaR forecasting. * Electronic address: emmanuel.bacry@polytechnique.fr † Electronic address: alexey@cmapx.polytechnique.fr ‡ Electronic address: muzy@univ-corse.fr","downloadable_attachments":[{"id":40495606,"asset_id":19215330,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":39443311,"first_name":"Jean-françois","last_name":"Muzy","domain_name":"independent","page_name":"JeanfrançoisMuzy","display_name":"Jean-françois Muzy","profile_url":"https://independent.academia.edu/Jeanfran%C3%A7oisMuzy?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":6208,"name":"Economic Theory","url":"https://www.academia.edu/Documents/in/Economic_Theory?f_ri=61227","nofollow":true},{"id":27659,"name":"Applied Economics","url":"https://www.academia.edu/Documents/in/Applied_Economics?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true},{"id":78086,"name":"Random Walk","url":"https://www.academia.edu/Documents/in/Random_Walk?f_ri=61227","nofollow":true},{"id":79964,"name":"Volatility Forecasting","url":"https://www.academia.edu/Documents/in/Volatility_Forecasting?f_ri=61227"},{"id":87533,"name":"Stochastic Volatility","url":"https://www.academia.edu/Documents/in/Stochastic_Volatility?f_ri=61227"},{"id":94431,"name":"Value at Risk","url":"https://www.academia.edu/Documents/in/Value_at_Risk?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_18319517" data-work_id="18319517" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/18319517/Exact_Maximum_Likelihood_estimator_for_the_BL_GARCH_model_under_elliptical_distributed_innovations">Exact Maximum Likelihood estimator for the BL-GARCH model under elliptical distributed innovations</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We are interested in the parametric class of Bilinear GARCH (BL-GARCH) models which are capable of simultaneously capturing the well known properties of financial retrun se- ries, volatility clustering and leverage effects. Specifically,... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_18319517" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We are interested in the parametric class of Bilinear GARCH (BL-GARCH) models which are capable of simultaneously capturing the well known properties of financial retrun se- ries, volatility clustering and leverage effects. Specifically, as it is often observed that the distribution of many financial time series data has heavy tails, heavier than the Normal distribution, we examine, in this paper, the BL-GARCH model in a general setting under some non-normal distributions. We also propose and implement a maximum likelihood estimation (MLE) methodology for parameter estimation. To evaluate the small-sample performance of this method for various models, a Monte Carlo study is conducted. Finally, the capability of within-sample estimation, using the S&P 500 daily returns, is also studied.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/18319517" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f98b277e51aff04820a2f52a3587dae8" rel="nofollow" data-download="{"attachment_id":42178771,"asset_id":18319517,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/42178771/download_file?st=MTc0MDU3MTkzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="38309892" href="https://independent.academia.edu/AbdouDiongue">Abdou Diongue</a><script data-card-contents-for-user="38309892" type="text/json">{"id":38309892,"first_name":"Abdou","last_name":"Diongue","domain_name":"independent","page_name":"AbdouDiongue","display_name":"Abdou Diongue","profile_url":"https://independent.academia.edu/AbdouDiongue?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_18319517 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="18319517"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 18319517, container: ".js-paper-rank-work_18319517", }); 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$(".js-view-count[data-work-id=18319517]").text(description); $(".js-view-count-work_18319517").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_18319517").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="18319517"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="39920" rel="nofollow" href="https://www.academia.edu/Documents/in/Parameter_estimation">Parameter estimation</a>, <script data-card-contents-for-ri="39920" type="text/json">{"id":39920,"name":"Parameter estimation","url":"https://www.academia.edu/Documents/in/Parameter_estimation?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="61227" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_time_series">Financial time series</a>, <script data-card-contents-for-ri="61227" type="text/json">{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="87364" rel="nofollow" href="https://www.academia.edu/Documents/in/Maximum_Likelihood">Maximum Likelihood</a>, <script data-card-contents-for-ri="87364" type="text/json">{"id":87364,"name":"Maximum Likelihood","url":"https://www.academia.edu/Documents/in/Maximum_Likelihood?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="805001" rel="nofollow" href="https://www.academia.edu/Documents/in/Small_samples">Small samples</a><script data-card-contents-for-ri="805001" type="text/json">{"id":805001,"name":"Small samples","url":"https://www.academia.edu/Documents/in/Small_samples?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=18319517]'), work: {"id":18319517,"title":"Exact Maximum Likelihood estimator for the BL-GARCH model under elliptical distributed innovations","created_at":"2015-11-14T03:56:25.294-08:00","url":"https://www.academia.edu/18319517/Exact_Maximum_Likelihood_estimator_for_the_BL_GARCH_model_under_elliptical_distributed_innovations?f_ri=61227","dom_id":"work_18319517","summary":"We are interested in the parametric class of Bilinear GARCH (BL-GARCH) models which are capable of simultaneously capturing the well known properties of financial retrun se- ries, volatility clustering and leverage effects. Specifically, as it is often observed that the distribution of many financial time series data has heavy tails, heavier than the Normal distribution, we examine, in this paper, the BL-GARCH model in a general setting under some non-normal distributions. We also propose and implement a maximum likelihood estimation (MLE) methodology for parameter estimation. To evaluate the small-sample performance of this method for various models, a Monte Carlo study is conducted. Finally, the capability of within-sample estimation, using the S\u0026P 500 daily returns, is also studied.","downloadable_attachments":[{"id":42178771,"asset_id":18319517,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":38309892,"first_name":"Abdou","last_name":"Diongue","domain_name":"independent","page_name":"AbdouDiongue","display_name":"Abdou Diongue","profile_url":"https://independent.academia.edu/AbdouDiongue?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":39920,"name":"Parameter estimation","url":"https://www.academia.edu/Documents/in/Parameter_estimation?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true},{"id":87364,"name":"Maximum Likelihood","url":"https://www.academia.edu/Documents/in/Maximum_Likelihood?f_ri=61227","nofollow":true},{"id":805001,"name":"Small samples","url":"https://www.academia.edu/Documents/in/Small_samples?f_ri=61227","nofollow":true},{"id":1142720,"name":"Normal Distribution","url":"https://www.academia.edu/Documents/in/Normal_Distribution?f_ri=61227"},{"id":1231367,"name":"Elliptical Distribution Family","url":"https://www.academia.edu/Documents/in/Elliptical_Distribution_Family?f_ri=61227"},{"id":1333436,"name":"Monte Carlo Method","url":"https://www.academia.edu/Documents/in/Monte_Carlo_Method?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_13119855" data-work_id="13119855" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/13119855/A_Markov_model_of_financial_returns">A Markov model of financial returns</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We address the general problem of how to quantify the kinematics of time series with stationary first moments but having non stationary multifractal long-range correlated second moments. We show that a Markov process is sufficient to... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_13119855" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We address the general problem of how to quantify the kinematics of time series with stationary first moments but having non stationary multifractal long-range correlated second moments. We show that a Markov process is sufficient to model important aspects of the multifractality observed in financial time series and propose a kinematic model of price fluctuations. We test the proposed model by analyzing index closing prices of the New York Stock Exchange and the DEM/USD tick-by-tick exchange rates obtained from Reuters EFX. We show that the model captures the characteristic features observed in actual financial time series, including volatility clustering, time scaling and fat tails in the probability density functions, power-law behavior of volatility correlations and, most importantly, the observed nonuniversal multifractal singularity spectrum. Motivated by our finding of strong agreement between the model and the data, we argue that at least two independent stochastic Gaussian variables are required to adequately model price fluctuations. r</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/13119855" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="70b29fa9e4d810bccaf6825ab92d5654" rel="nofollow" data-download="{"attachment_id":45670970,"asset_id":13119855,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/45670970/download_file?st=MTc0MDU3MTkzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="32373638" href="https://independent.academia.edu/MServa">M. 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We show that a Markov process is sufficient to model important aspects of the multifractality observed in financial time series and propose a kinematic model of price fluctuations. We test the proposed model by analyzing index closing prices of the New York Stock Exchange and the DEM/USD tick-by-tick exchange rates obtained from Reuters EFX. We show that the model captures the characteristic features observed in actual financial time series, including volatility clustering, time scaling and fat tails in the probability density functions, power-law behavior of volatility correlations and, most importantly, the observed nonuniversal multifractal singularity spectrum. Motivated by our finding of strong agreement between the model and the data, we argue that at least two independent stochastic Gaussian variables are required to adequately model price fluctuations. r","downloadable_attachments":[{"id":45670970,"asset_id":13119855,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":32373638,"first_name":"M.","last_name":"Serva","domain_name":"independent","page_name":"MServa","display_name":"M. Serva","profile_url":"https://independent.academia.edu/MServa?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":318,"name":"Mathematical Physics","url":"https://www.academia.edu/Documents/in/Mathematical_Physics?f_ri=61227","nofollow":true},{"id":518,"name":"Quantum Physics","url":"https://www.academia.edu/Documents/in/Quantum_Physics?f_ri=61227","nofollow":true},{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true},{"id":30485,"name":"Time series analysis","url":"https://www.academia.edu/Documents/in/Time_series_analysis?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227"},{"id":113890,"name":"Power Law","url":"https://www.academia.edu/Documents/in/Power_Law?f_ri=61227"},{"id":228986,"name":"Exchange rate","url":"https://www.academia.edu/Documents/in/Exchange_rate?f_ri=61227"},{"id":741671,"name":"Markov Process","url":"https://www.academia.edu/Documents/in/Markov_Process?f_ri=61227"},{"id":749302,"name":"Indexation","url":"https://www.academia.edu/Documents/in/Indexation?f_ri=61227"},{"id":871028,"name":"Singular-spectrum Analysis","url":"https://www.academia.edu/Documents/in/Singular-spectrum_Analysis?f_ri=61227"},{"id":872399,"name":"Probability Density Function","url":"https://www.academia.edu/Documents/in/Probability_Density_Function?f_ri=61227"},{"id":899955,"name":"New York Stock Exchange","url":"https://www.academia.edu/Documents/in/New_York_Stock_Exchange?f_ri=61227"},{"id":2050770,"name":"Markov model","url":"https://www.academia.edu/Documents/in/Markov_model?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_15470705" data-work_id="15470705" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/15470705/Hedging_the_black_swan_Conditional_heteroskedasticity_and_tail_dependence_in_S_and_amp_P500_and_VIX">Hedging the black swan: Conditional heteroskedasticity and tail dependence in S&amp;P500 and VIX</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">JEL classification: C14 C18 G13</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/15470705" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="63cc3a07dbd5d512939d40762241b096" rel="nofollow" data-download="{"attachment_id":43160905,"asset_id":15470705,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43160905/download_file?st=MTc0MDU3MTkzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="34614571" href="https://independent.academia.edu/SerHuangPoon">Ser-Huang Poon</a><script data-card-contents-for-user="34614571" type="text/json">{"id":34614571,"first_name":"Ser-Huang","last_name":"Poon","domain_name":"independent","page_name":"SerHuangPoon","display_name":"Ser-Huang Poon","profile_url":"https://independent.academia.edu/SerHuangPoon?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_15470705 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="15470705"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 15470705, container: ".js-paper-rank-work_15470705", }); 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Mandelbrot&#x27;s 80st birthday, to appear in Wilmott magazine. We discuss how multiplicative cascades and related multifractal ideas might be relevant to model the main statistical features of... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_19215323" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This is a short review in honor of B. Mandelbrot&#x27;s 80st birthday, to appear in Wilmott magazine. We discuss how multiplicative cascades and related multifractal ideas might be relevant to model the main statistical features of financial time series, in particular the intermit-tent, ...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/19215323" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ce8ff511ede96c2ad8e0602bd9b45235" rel="nofollow" data-download="{"attachment_id":40495596,"asset_id":19215323,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/40495596/download_file?st=MTc0MDU3MTkzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="39554629" href="https://independent.academia.edu/LBorland">L. Borland</a><script data-card-contents-for-user="39554629" type="text/json">{"id":39554629,"first_name":"L.","last_name":"Borland","domain_name":"independent","page_name":"LBorland","display_name":"L. Borland","profile_url":"https://independent.academia.edu/LBorland?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-19215323">+2</span><div class="hidden js-additional-users-19215323"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/Jeanfran%C3%A7oisMuzy">Jean-françois Muzy</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://cnrs.academia.edu/JfMuzy">J.-f. Muzy</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-19215323'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-19215323').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_19215323 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="19215323"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 19215323, container: ".js-paper-rank-work_19215323", }); });</script></li><li class="js-percentile-work_19215323 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 19215323; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_19215323"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_19215323 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="19215323"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 19215323; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=19215323]").text(description); $(".js-view-count-work_19215323").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_19215323").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="19215323"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="520" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistical_Mechanics">Statistical Mechanics</a>, <script data-card-contents-for-ri="520" type="text/json">{"id":520,"name":"Statistical Mechanics","url":"https://www.academia.edu/Documents/in/Statistical_Mechanics?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="45042" rel="nofollow" href="https://www.academia.edu/Documents/in/Long_Memory">Long Memory</a>, <script data-card-contents-for-ri="45042" type="text/json">{"id":45042,"name":"Long Memory","url":"https://www.academia.edu/Documents/in/Long_Memory?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="61227" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_time_series">Financial time series</a>, <script data-card-contents-for-ri="61227" type="text/json">{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="78086" rel="nofollow" href="https://www.academia.edu/Documents/in/Random_Walk">Random Walk</a><script data-card-contents-for-ri="78086" type="text/json">{"id":78086,"name":"Random Walk","url":"https://www.academia.edu/Documents/in/Random_Walk?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=19215323]'), work: {"id":19215323,"title":"The Dynamics of Financial Markets--Mandelbrot's multifractal cascades, and beyond","created_at":"2015-11-30T00:56:10.718-08:00","url":"https://www.academia.edu/19215323/The_Dynamics_of_Financial_Markets_Mandelbrots_multifractal_cascades_and_beyond?f_ri=61227","dom_id":"work_19215323","summary":"This is a short review in honor of B. Mandelbrot\u0026#x27;s 80st birthday, to appear in Wilmott magazine. We discuss how multiplicative cascades and related multifractal ideas might be relevant to model the main statistical features of financial time series, in particular the intermit-tent, ...","downloadable_attachments":[{"id":40495596,"asset_id":19215323,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":39554629,"first_name":"L.","last_name":"Borland","domain_name":"independent","page_name":"LBorland","display_name":"L. Borland","profile_url":"https://independent.academia.edu/LBorland?f_ri=61227","photo":"/images/s65_no_pic.png"},{"id":39443311,"first_name":"Jean-françois","last_name":"Muzy","domain_name":"independent","page_name":"JeanfrançoisMuzy","display_name":"Jean-françois Muzy","profile_url":"https://independent.academia.edu/Jeanfran%C3%A7oisMuzy?f_ri=61227","photo":"/images/s65_no_pic.png"},{"id":40578760,"first_name":"J.-f.","last_name":"Muzy","domain_name":"cnrs","page_name":"JfMuzy","display_name":"J.-f. Muzy","profile_url":"https://cnrs.academia.edu/JfMuzy?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":520,"name":"Statistical Mechanics","url":"https://www.academia.edu/Documents/in/Statistical_Mechanics?f_ri=61227","nofollow":true},{"id":45042,"name":"Long Memory","url":"https://www.academia.edu/Documents/in/Long_Memory?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true},{"id":78086,"name":"Random Walk","url":"https://www.academia.edu/Documents/in/Random_Walk?f_ri=61227","nofollow":true},{"id":270673,"name":"Financial Market","url":"https://www.academia.edu/Documents/in/Financial_Market?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_8314247" data-work_id="8314247" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/8314247/Bayesian_Dynamic_Factor_Models_and_Portfolio_Allocation">Bayesian Dynamic Factor Models and Portfolio Allocation</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We discuss the development of dynamic factor models for multivariate financial time series, and the incorporation of stochastic volatility components for latent factor processes. Bayesian inference and computation is developed and... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_8314247" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We discuss the development of dynamic factor models for multivariate financial time series, and the incorporation of stochastic volatility components for latent factor processes. Bayesian inference and computation is developed and explored in a study of the dynamic factor structure of daily spot exchange rates for a selection of international currencies. The models are direct generalisations of univariate stochastic volatility models, and represent specific varieties of models recently discussed in the growing multivariate stochastic volatility literature. We discuss model fitting based on retrospective data and sequential analysis for forward filtering and short-term forecasting. Analyses are compared with results from the much simpler method of dynamic variance matrix discounting that, for over a decade, has been a standard approach in applied financial econometrics. We study these models in analysis, forecasting and sequential portfolio allocation for a selected set of international exchange rate return time series. Our goals are to understand a range of modelling questions arising in using these factor models, and to explore empirical performance in portfolio construction relative to discount approaches. We report on our experiences and conclude with comments about the practical utility of structured factor models, and on future potential model extensions.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/8314247" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f3f9dd70251c6c3d2cef3c56c6768c69" rel="nofollow" data-download="{"attachment_id":34722097,"asset_id":8314247,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/34722097/download_file?st=MTc0MDU3MTkzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="16578845" href="https://independent.academia.edu/OmarAguilar12">Omar Aguilar</a><script data-card-contents-for-user="16578845" type="text/json">{"id":16578845,"first_name":"Omar","last_name":"Aguilar","domain_name":"independent","page_name":"OmarAguilar12","display_name":"Omar Aguilar","profile_url":"https://independent.academia.edu/OmarAguilar12?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_8314247 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="8314247"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 8314247, container: ".js-paper-rank-work_8314247", }); });</script></li><li class="js-percentile-work_8314247 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 8314247; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_8314247"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_8314247 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="8314247"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 8314247; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=8314247]").text(description); $(".js-view-count-work_8314247").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_8314247").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="8314247"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">15</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="724" rel="nofollow" href="https://www.academia.edu/Documents/in/Economics">Economics</a>, <script data-card-contents-for-ri="724" type="text/json">{"id":724,"name":"Economics","url":"https://www.academia.edu/Documents/in/Economics?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4456" rel="nofollow" href="https://www.academia.edu/Documents/in/Time_Series">Time Series</a>, <script data-card-contents-for-ri="4456" type="text/json">{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="51529" rel="nofollow" href="https://www.academia.edu/Documents/in/Bayesian_Inference">Bayesian Inference</a>, <script data-card-contents-for-ri="51529" type="text/json">{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="61227" rel="nofollow" href="https://www.academia.edu/Documents/in/Financial_time_series">Financial time series</a><script data-card-contents-for-ri="61227" type="text/json">{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=8314247]'), work: {"id":8314247,"title":"Bayesian Dynamic Factor Models and Portfolio Allocation","created_at":"2014-09-13T16:47:10.285-07:00","url":"https://www.academia.edu/8314247/Bayesian_Dynamic_Factor_Models_and_Portfolio_Allocation?f_ri=61227","dom_id":"work_8314247","summary":"We discuss the development of dynamic factor models for multivariate financial time series, and the incorporation of stochastic volatility components for latent factor processes. Bayesian inference and computation is developed and explored in a study of the dynamic factor structure of daily spot exchange rates for a selection of international currencies. The models are direct generalisations of univariate stochastic volatility models, and represent specific varieties of models recently discussed in the growing multivariate stochastic volatility literature. We discuss model fitting based on retrospective data and sequential analysis for forward filtering and short-term forecasting. Analyses are compared with results from the much simpler method of dynamic variance matrix discounting that, for over a decade, has been a standard approach in applied financial econometrics. We study these models in analysis, forecasting and sequential portfolio allocation for a selected set of international exchange rate return time series. Our goals are to understand a range of modelling questions arising in using these factor models, and to explore empirical performance in portfolio construction relative to discount approaches. We report on our experiences and conclude with comments about the practical utility of structured factor models, and on future potential model extensions.","downloadable_attachments":[{"id":34722097,"asset_id":8314247,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":16578845,"first_name":"Omar","last_name":"Aguilar","domain_name":"independent","page_name":"OmarAguilar12","display_name":"Omar Aguilar","profile_url":"https://independent.academia.edu/OmarAguilar12?f_ri=61227","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":724,"name":"Economics","url":"https://www.academia.edu/Documents/in/Economics?f_ri=61227","nofollow":true},{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true},{"id":51529,"name":"Bayesian Inference","url":"https://www.academia.edu/Documents/in/Bayesian_Inference?f_ri=61227","nofollow":true},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227","nofollow":true},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences?f_ri=61227"},{"id":81635,"name":"Dynamic factor analysis","url":"https://www.academia.edu/Documents/in/Dynamic_factor_analysis?f_ri=61227"},{"id":85262,"name":"Markov Chain Monte Carlo","url":"https://www.academia.edu/Documents/in/Markov_Chain_Monte_Carlo?f_ri=61227"},{"id":87533,"name":"Stochastic Volatility","url":"https://www.academia.edu/Documents/in/Stochastic_Volatility?f_ri=61227"},{"id":228986,"name":"Exchange rate","url":"https://www.academia.edu/Documents/in/Exchange_rate?f_ri=61227"},{"id":425240,"name":"Sequential Analysis","url":"https://www.academia.edu/Documents/in/Sequential_Analysis?f_ri=61227"},{"id":514741,"name":"Five Factor Model","url":"https://www.academia.edu/Documents/in/Five_Factor_Model?f_ri=61227"},{"id":598616,"name":"Portfolio allocation","url":"https://www.academia.edu/Documents/in/Portfolio_allocation?f_ri=61227"},{"id":886557,"name":"Portfolio Selection","url":"https://www.academia.edu/Documents/in/Portfolio_Selection?f_ri=61227"},{"id":1033644,"name":"Factor structure","url":"https://www.academia.edu/Documents/in/Factor_structure?f_ri=61227"},{"id":1590508,"name":"Dynamic linear model","url":"https://www.academia.edu/Documents/in/Dynamic_linear_model?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_23456788" data-work_id="23456788" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/23456788/Financial_Market_Trading_System_With_a_Hierarchical_Coevolutionary_Fuzzy_Predictive_Model">Financial Market Trading System With a Hierarchical Coevolutionary Fuzzy Predictive Model</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Financial market prediction and trading presents a challenging task that attracts great interest from researchers and investors because success may result in substantial rewards. This paper describes the application of a hierarchical... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_23456788" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Financial market prediction and trading presents a challenging task that attracts great interest from researchers and investors because success may result in substantial rewards. This paper describes the application of a hierarchical coevolutionary fuzzy system called HiCEFS for predicting financial time series. A novel financial trading system using HiCEFS as a predictive model and employing a prudent trading strategy based on the price percentage oscillator (PPO) is proposed. In order to construct an accurate predictive model, a form of generic membership function named Irregular Shaped Membership Function (ISMF) is employed and a hierarchical coevolutionary genetic algorithm (HCGA) is adopted to automatically derive the ISMFs for each input feature in HiCEFS. With the accurate prediction from HiCEFS and the prudent trading strategy, the proposed system outperforms the simple buy-and-hold strategy, the trading system without prediction and the trading system with other predictive models (EFuNN, DENFIS and RSPOP) on real-world financial data.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/23456788" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c448f596fde57ba41b903318d3dab1b9" rel="nofollow" data-download="{"attachment_id":43896226,"asset_id":23456788,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43896226/download_file?st=MTc0MDU3MTkzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="45429966" href="https://aus.academia.edu/MPasquier">M. Pasquier</a><script data-card-contents-for-user="45429966" type="text/json">{"id":45429966,"first_name":"M.","last_name":"Pasquier","domain_name":"aus","page_name":"MPasquier","display_name":"M. Pasquier","profile_url":"https://aus.academia.edu/MPasquier?f_ri=61227","photo":"https://0.academia-photos.com/45429966/18075766/18074077/s65_m..pasquier.jpg"}</script></span></span></li><li class="js-paper-rank-work_23456788 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="23456788"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 23456788, container: ".js-paper-rank-work_23456788", }); });</script></li><li class="js-percentile-work_23456788 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 23456788; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_23456788"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_23456788 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="23456788"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 23456788; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=23456788]").text(description); $(".js-view-count-work_23456788").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_23456788").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="23456788"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">22</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="37" rel="nofollow" href="https://www.academia.edu/Documents/in/Information_Systems">Information Systems</a>, <script data-card-contents-for-ri="37" type="text/json">{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4165" rel="nofollow" href="https://www.academia.edu/Documents/in/Fuzzy_Logic">Fuzzy Logic</a>, <script data-card-contents-for-ri="4165" type="text/json">{"id":4165,"name":"Fuzzy Logic","url":"https://www.academia.edu/Documents/in/Fuzzy_Logic?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4304" rel="nofollow" href="https://www.academia.edu/Documents/in/Coevolution">Coevolution</a>, <script data-card-contents-for-ri="4304" type="text/json">{"id":4304,"name":"Coevolution","url":"https://www.academia.edu/Documents/in/Coevolution?f_ri=61227","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4456" rel="nofollow" href="https://www.academia.edu/Documents/in/Time_Series">Time Series</a><script data-card-contents-for-ri="4456" type="text/json">{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=23456788]'), work: {"id":23456788,"title":"Financial Market Trading System With a Hierarchical Coevolutionary Fuzzy Predictive Model","created_at":"2016-03-19T11:07:27.932-07:00","url":"https://www.academia.edu/23456788/Financial_Market_Trading_System_With_a_Hierarchical_Coevolutionary_Fuzzy_Predictive_Model?f_ri=61227","dom_id":"work_23456788","summary":"Financial market prediction and trading presents a challenging task that attracts great interest from researchers and investors because success may result in substantial rewards. This paper describes the application of a hierarchical coevolutionary fuzzy system called HiCEFS for predicting financial time series. A novel financial trading system using HiCEFS as a predictive model and employing a prudent trading strategy based on the price percentage oscillator (PPO) is proposed. In order to construct an accurate predictive model, a form of generic membership function named Irregular Shaped Membership Function (ISMF) is employed and a hierarchical coevolutionary genetic algorithm (HCGA) is adopted to automatically derive the ISMFs for each input feature in HiCEFS. With the accurate prediction from HiCEFS and the prudent trading strategy, the proposed system outperforms the simple buy-and-hold strategy, the trading system without prediction and the trading system with other predictive models (EFuNN, DENFIS and RSPOP) on real-world financial data.","downloadable_attachments":[{"id":43896226,"asset_id":23456788,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":45429966,"first_name":"M.","last_name":"Pasquier","domain_name":"aus","page_name":"MPasquier","display_name":"M. Pasquier","profile_url":"https://aus.academia.edu/MPasquier?f_ri=61227","photo":"https://0.academia-photos.com/45429966/18075766/18074077/s65_m..pasquier.jpg"}],"research_interests":[{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems?f_ri=61227","nofollow":true},{"id":4165,"name":"Fuzzy Logic","url":"https://www.academia.edu/Documents/in/Fuzzy_Logic?f_ri=61227","nofollow":true},{"id":4304,"name":"Coevolution","url":"https://www.academia.edu/Documents/in/Coevolution?f_ri=61227","nofollow":true},{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series?f_ri=61227","nofollow":true},{"id":5026,"name":"Genetic Algorithms","url":"https://www.academia.edu/Documents/in/Genetic_Algorithms?f_ri=61227"},{"id":5394,"name":"Fuzzy set theory","url":"https://www.academia.edu/Documents/in/Fuzzy_set_theory?f_ri=61227"},{"id":6177,"name":"Modeling","url":"https://www.academia.edu/Documents/in/Modeling?f_ri=61227"},{"id":30329,"name":"Genetic Algorithm","url":"https://www.academia.edu/Documents/in/Genetic_Algorithm?f_ri=61227"},{"id":61227,"name":"Financial time series","url":"https://www.academia.edu/Documents/in/Financial_time_series?f_ri=61227"},{"id":78434,"name":"Investment","url":"https://www.academia.edu/Documents/in/Investment?f_ri=61227"},{"id":174781,"name":"Oscillations","url":"https://www.academia.edu/Documents/in/Oscillations?f_ri=61227"},{"id":178021,"name":"Filter","url":"https://www.academia.edu/Documents/in/Filter?f_ri=61227"},{"id":212758,"name":"Trend Analysis","url":"https://www.academia.edu/Documents/in/Trend_Analysis?f_ri=61227"},{"id":224767,"name":"Prediction Model","url":"https://www.academia.edu/Documents/in/Prediction_Model?f_ri=61227"},{"id":270673,"name":"Financial Market","url":"https://www.academia.edu/Documents/in/Financial_Market?f_ri=61227"},{"id":284267,"name":"Membership Function","url":"https://www.academia.edu/Documents/in/Membership_Function?f_ri=61227"},{"id":292710,"name":"Inversion","url":"https://www.academia.edu/Documents/in/Inversion?f_ri=61227"},{"id":311931,"name":"STOCK EXCHANGE","url":"https://www.academia.edu/Documents/in/STOCK_EXCHANGE?f_ri=61227"},{"id":314271,"name":"Fuzzy System","url":"https://www.academia.edu/Documents/in/Fuzzy_System?f_ri=61227"},{"id":402530,"name":"Trading System","url":"https://www.academia.edu/Documents/in/Trading_System?f_ri=61227"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering?f_ri=61227"},{"id":2036700,"name":"Trading Strategy","url":"https://www.academia.edu/Documents/in/Trading_Strategy?f_ri=61227"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_22559434" data-work_id="22559434" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/22559434/Power_Laws_in_Financial_Time_Series">Power Laws in Financial Time Series</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We attempt empirical detection and characterization of power laws in financial time series. Fractional Brownian motion is defined. After testing for multifractality we calculate the multifractal spectrum of the series. The multifractal... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_22559434" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We attempt empirical detection and characterization of power laws in financial time series. Fractional Brownian motion is defined. After testing for multifractality we calculate the multifractal spectrum of the series. The multifractal nature of stock prices leads to volatility clus- tering (conditional heteroscedasticity) and long memory (slowly decaying autocorrelation). Wavelet Transform Modulus Maxima approach to mul- tifractal spectrum estimation proved</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/22559434" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="2fc4e879cabd7574ab03477b4491e0ce" rel="nofollow" data-download="{"attachment_id":43170331,"asset_id":22559434,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43170331/download_file?st=MTc0MDU3MTkzMSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="44020124" href="https://independent.academia.edu/ChenweiWang1">Chenwei Wang</a><script data-card-contents-for-user="44020124" type="text/json">{"id":44020124,"first_name":"Chenwei","last_name":"Wang","domain_name":"independent","page_name":"ChenweiWang1","display_name":"Chenwei Wang","profile_url":"https://independent.academia.edu/ChenweiWang1?f_ri=61227","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_22559434 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="22559434"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 22559434, container: ".js-paper-rank-work_22559434", }); 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