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volatility</title> <meta name="description" content="Search results for: realized volatility"> <meta name="keywords" content="realized volatility"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="realized volatility"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 938</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: realized volatility</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">938</span> Normalizing Logarithms of Realized Volatility in an ARFIMA Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20L.%20C.%20Yap">G. L. C. Yap</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Modelling realized volatility with high-frequency returns is popular as it is an unbiased and efficient estimator of return volatility. A computationally simple model is fitting the logarithms of the realized volatilities with a fractionally integrated long-memory Gaussian process. The Gaussianity assumption simplifies the parameter estimation using the Whittle approximation. Nonetheless, this assumption may not be met in the finite samples and there may be a need to normalize the financial series. Based on the empirical indices S&P500 and DAX, this paper examines the performance of the linear volatility model pre-treated with normalization compared to its existing counterpart. The empirical results show that by including normalization as a pre-treatment procedure, the forecast performance outperforms the existing model in terms of statistical and economic evaluations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process" title="Gaussian process">Gaussian process</a>, <a href="https://publications.waset.org/abstracts/search?q=long-memory" title=" long-memory"> long-memory</a>, <a href="https://publications.waset.org/abstracts/search?q=normalization" title=" normalization"> normalization</a>, <a href="https://publications.waset.org/abstracts/search?q=value-at-risk" title=" value-at-risk"> value-at-risk</a>, <a href="https://publications.waset.org/abstracts/search?q=volatility" title=" volatility"> volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=Whittle%20estimator" title=" Whittle estimator"> Whittle estimator</a> </p> <a href="https://publications.waset.org/abstracts/58573/normalizing-logarithms-of-realized-volatility-in-an-arfima-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58573.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">354</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">937</span> Superiority of High Frequency Based Volatility Models: Empirical Evidence from an Emerging Market</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sibel%20Celik">Sibel Celik</a>, <a href="https://publications.waset.org/abstracts/search?q=H%C3%BCseyin%20Ergin"> Hüseyin Ergin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper aims to find the best volatility forecasting model for stock markets in Turkey. For this purpose, we compare performance of different volatility models-both traditional GARCH model and high frequency based volatility models- and conclude that both in pre-crisis and crisis period, the performance of high frequency based volatility models are better than traditional GARCH model. The findings of paper are important for policy makers, financial institutions and investors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=volatility" title="volatility">volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=GARCH%20model" title=" GARCH model"> GARCH model</a>, <a href="https://publications.waset.org/abstracts/search?q=realized%20volatility" title=" realized volatility"> realized volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20frequency%20data" title=" high frequency data"> high frequency data</a> </p> <a href="https://publications.waset.org/abstracts/18459/superiority-of-high-frequency-based-volatility-models-empirical-evidence-from-an-emerging-market" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18459.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">486</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">936</span> Islamic Equity Markets Response to Volatility of Bitcoin</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zakaria%20S.%20G.%20Hegazy">Zakaria S. G. Hegazy</a>, <a href="https://publications.waset.org/abstracts/search?q=Walid%20M.%20A.%20Ahmed"> Walid M. A. Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper examines the dependence structure of Islamic stock markets on Bitcoin’s realized volatility components in bear, normal, and bull market periods. A quantile regression approach is employed, after adjusting raw returns with respect to a broad set of relevant global factors and accounting for structural breaks in the data. The results reveal that upside volatility tends to exert negative influences on Islamic developed-market returns more in bear than in bull market conditions, while downside volatility positively affects returns during bear and bull conditions. For emerging markets, we find that the upside (downside) component exerts lagged negative (positive) effects on returns in bear (all) market regimes. By and large, the dependence structures turn out to be asymmetric. Our evidence provides essential implications for investors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cryptocurrency%20markets" title="cryptocurrency markets">cryptocurrency markets</a>, <a href="https://publications.waset.org/abstracts/search?q=bitcoin" title=" bitcoin"> bitcoin</a>, <a href="https://publications.waset.org/abstracts/search?q=realized%20volatility%20measures" title=" realized volatility measures"> realized volatility measures</a>, <a href="https://publications.waset.org/abstracts/search?q=asymmetry" title=" asymmetry"> asymmetry</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20regression" title=" quantile regression"> quantile regression</a> </p> <a href="https://publications.waset.org/abstracts/141351/islamic-equity-markets-response-to-volatility-of-bitcoin" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141351.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">187</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">935</span> Statistical Inferences for GQARCH-It\^{o} - Jumps Model Based on The Realized Range Volatility</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fu%20Jinyu">Fu Jinyu</a>, <a href="https://publications.waset.org/abstracts/search?q=Lin%20Jinguan"> Lin Jinguan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces a novel approach that unifies two types of models: one is the continuous-time jump-diffusion used to model high-frequency data, and the other is discrete-time GQARCH employed to model low-frequency financial data by embedding the discrete GQARCH structure with jumps in the instantaneous volatility process. This model is named “GQARCH-It\^{o} -Jumps mode.” We adopt the realized range-based threshold estimation for high-frequency financial data rather than the realized return-based volatility estimators, which entail the loss of intra-day information of the price movement. Meanwhile, a quasi-likelihood function for the low-frequency GQARCH structure with jumps is developed for the parametric estimate. The asymptotic theories are mainly established for the proposed estimators in the case of finite activity jumps. Moreover, simulation studies are implemented to check the finite sample performance of the proposed methodology. Specifically, it is demonstrated that how our proposed approaches can be practically used on some financial data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=It%5C%5E%7Bo%7D%20process" title="It\^{o} process">It\^{o} process</a>, <a href="https://publications.waset.org/abstracts/search?q=GQARCH" title=" GQARCH"> GQARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=leverage%20effects" title=" leverage effects"> leverage effects</a>, <a href="https://publications.waset.org/abstracts/search?q=threshold" title=" threshold"> threshold</a>, <a href="https://publications.waset.org/abstracts/search?q=realized%20range-based%20volatility%20estimator" title=" realized range-based volatility estimator"> realized range-based volatility estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=quasi-maximum%20likelihood%20estimate" title=" quasi-maximum likelihood estimate"> quasi-maximum likelihood estimate</a> </p> <a href="https://publications.waset.org/abstracts/144833/statistical-inferences-for-gqarch-ito-jumps-model-based-on-the-realized-range-volatility" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144833.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">155</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">934</span> Bayesian Value at Risk Forecast Using Realized Conditional Autoregressive Expectiel Mdodel with an Application of Cryptocurrency</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Niya%20Chen">Niya Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Jennifer%20Chan"> Jennifer Chan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the financial market, risk management helps to minimize potential loss and maximize profit. There are two ways to assess risks; the first way is to calculate the risk directly based on the volatility. The most common risk measurements are Value at Risk (VaR), sharp ratio, and beta. Alternatively, we could look at the quantile of the return to assess the risk. Popular return models such as GARCH and stochastic volatility (SV) focus on modeling the mean of the return distribution via capturing the volatility dynamics; however, the quantile/expectile method will give us an idea of the distribution with the extreme return value. It will allow us to forecast VaR using return which is direct information. The advantage of using these non-parametric methods is that it is not bounded by the distribution assumptions from the parametric method. But the difference between them is that expectile uses a second-order loss function while quantile regression uses a first-order loss function. We consider several quantile functions, different volatility measures, and estimates from some volatility models. To estimate the expectile of the model, we use Realized Conditional Autoregressive Expectile (CARE) model with the bayesian method to achieve this. We would like to see if our proposed models outperform existing models in cryptocurrency, and we will test it by using Bitcoin mainly as well as Ethereum. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=expectile" title="expectile">expectile</a>, <a href="https://publications.waset.org/abstracts/search?q=CARE%20Model" title=" CARE Model"> CARE Model</a>, <a href="https://publications.waset.org/abstracts/search?q=CARR%20Model" title=" CARR Model"> CARR Model</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile" title=" quantile"> quantile</a>, <a href="https://publications.waset.org/abstracts/search?q=cryptocurrency" title=" cryptocurrency"> cryptocurrency</a>, <a href="https://publications.waset.org/abstracts/search?q=Value%20at%20Risk" title=" Value at Risk"> Value at Risk</a> </p> <a href="https://publications.waset.org/abstracts/159362/bayesian-value-at-risk-forecast-using-realized-conditional-autoregressive-expectiel-mdodel-with-an-application-of-cryptocurrency" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159362.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">109</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">933</span> Calibration of Hybrid Model and Arbitrage-Free Implied Volatility Surface</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kun%20Huang">Kun Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper investigates whether the combination of local and stochastic volatility models can be calibrated exactly to any arbitrage-free implied volatility surface of European option. The risk neutral Brownian Bridge density is applied for calibration of the leverage function of our Hybrid model. Furthermore, the tails of marginal risk neutral density are generated by Generalized Extreme Value distribution in order to capture the properties of asset returns. The local volatility is generated from the arbitrage-free implied volatility surface using stochastic volatility inspired parameterization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arbitrage%20free%20implied%20volatility" title="arbitrage free implied volatility">arbitrage free implied volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=calibration" title=" calibration"> calibration</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20distribution" title=" extreme value distribution"> extreme value distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20model" title=" hybrid model"> hybrid model</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20volatility" title=" local volatility"> local volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=risk-neutral%20density" title=" risk-neutral density"> risk-neutral density</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20volatility" title=" stochastic volatility"> stochastic volatility</a> </p> <a href="https://publications.waset.org/abstracts/62414/calibration-of-hybrid-model-and-arbitrage-free-implied-volatility-surface" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62414.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">267</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">932</span> The Relationship between Top Management Replacement and Risk, Sale and Cash Volatilities with Respect to Unqualified Audit Opinion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehdi%20Dasineh">Mehdi Dasineh</a>, <a href="https://publications.waset.org/abstracts/search?q=Yadollah%20Tariverdi"> Yadollah Tariverdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Marzieh%20H.%20Takhti"> Marzieh H. Takhti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper investigated the relationship between top management turnover with risk volatility, sale volatility and fluctuations in the company's cash depending on the unqualified audit report in Tehran Stock Exchange (TSE). In this study, we examined 104 firms over the period 2009-2014 which were selected from (TSE). There was 624 observed year-company data in this research. Hypotheses of this research have been evaluated by using regression tests for example F-statistical and Durbin-Watson. Based on our sample we found significant relationship between top management replacement and risk volatility, sale Volatility and cash volatility with tendency unqualified audit opinion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=top%20management%20replacement" title="top management replacement">top management replacement</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20volatility" title=" risk volatility"> risk volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=sale%20volatility" title=" sale volatility"> sale volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=cash%20volatility" title=" cash volatility"> cash volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=unqualified%20audit%20opinion" title=" unqualified audit opinion"> unqualified audit opinion</a> </p> <a href="https://publications.waset.org/abstracts/45022/the-relationship-between-top-management-replacement-and-risk-sale-and-cash-volatilities-with-respect-to-unqualified-audit-opinion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45022.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">282</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">931</span> Volatility Spillover Among the Stock Markets of South Asian Countries</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tariq%20Aziz">Tariq Aziz</a>, <a href="https://publications.waset.org/abstracts/search?q=Suresh%20Kumar"> Suresh Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Vikesh%20Kumar"> Vikesh Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Sheraz%20Mustafa"> Sheraz Mustafa</a>, <a href="https://publications.waset.org/abstracts/search?q=Jhanzeb%20Marwat"> Jhanzeb Marwat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper provides an updated version of volatility spillover among the equity markets of South Asian countries, including Pakistan, India, Srilanka, and Bangladesh. The analysis uses both symmetric and asymmetric Generalized Autoregressive Conditional Heteroscedasticity models to investigate volatility persistence and leverage effect. The bivariate EGARCH model is used to test for volatility transmission between two equity markets. Weekly data for the period February 2013 to August 2019 is used for empirical analysis. The findings indicate that the leverage effect exists in the equity markets of all the countries except Bangladesh. The volatility spillover from the equity market of Bangladesh to all other countries is negative and significant whereas the volatility of the equity market of Sri-Lanka does influence the volatility of any other country’s equity market. Indian equity market influence only the volatility of the Sri-Lankan equity market; and there is bidirectional volatility spillover between the equity markets of Pakistan and Bangladesh. The findings are important for policy-makers and international investors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=volatility%20spillover" title="volatility spillover">volatility spillover</a>, <a href="https://publications.waset.org/abstracts/search?q=volatility%20persistence" title=" volatility persistence"> volatility persistence</a>, <a href="https://publications.waset.org/abstracts/search?q=garch" title=" garch"> garch</a>, <a href="https://publications.waset.org/abstracts/search?q=egarch" title=" egarch"> egarch</a> </p> <a href="https://publications.waset.org/abstracts/121891/volatility-spillover-among-the-stock-markets-of-south-asian-countries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/121891.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">139</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">930</span> VaR Estimation Using the Informational Content of Futures Traded Volume</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amel%20Oueslati">Amel Oueslati</a>, <a href="https://publications.waset.org/abstracts/search?q=Olfa%20Benouda"> Olfa Benouda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> New Value at Risk (VaR) estimation is proposed and investigated. The well-known two stages Garch-EVT approach uses conditional volatility to generate one step ahead forecasts of VaR. With daily data for twelve stocks that decompose the Dow Jones Industrial Average (DJIA) index, this paper incorporates the volume in the first stage volatility estimation. Afterwards, the forecasting ability of this conditional volatility concerning the VaR estimation is compared to that of a basic volatility model without considering any trading component. The results are significant and bring out the importance of the trading volume in the VaR measure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Garch-EVT" title="Garch-EVT">Garch-EVT</a>, <a href="https://publications.waset.org/abstracts/search?q=value%20at%20risk" title=" value at risk"> value at risk</a>, <a href="https://publications.waset.org/abstracts/search?q=volume" title=" volume"> volume</a>, <a href="https://publications.waset.org/abstracts/search?q=volatility" title=" volatility"> volatility</a> </p> <a href="https://publications.waset.org/abstracts/56021/var-estimation-using-the-informational-content-of-futures-traded-volume" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56021.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">285</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">929</span> Forecasting the Volatility of Geophysical Time Series with Stochastic Volatility Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maria%20C.%20Mariani">Maria C. Mariani</a>, <a href="https://publications.waset.org/abstracts/search?q=Md%20Al%20Masum%20Bhuiyan"> Md Al Masum Bhuiyan</a>, <a href="https://publications.waset.org/abstracts/search?q=Osei%20K.%20Tweneboah"> Osei K. Tweneboah</a>, <a href="https://publications.waset.org/abstracts/search?q=Hector%20G.%20Huizar"> Hector G. Huizar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work is devoted to the study of modeling geophysical time series. A stochastic technique with time-varying parameters is used to forecast the volatility of data arising in geophysics. In this study, the volatility is defined as a logarithmic first-order autoregressive process. We observe that the inclusion of log-volatility into the time-varying parameter estimation significantly improves forecasting which is facilitated via maximum likelihood estimation. This allows us to conclude that the estimation algorithm for the corresponding one-step-ahead suggested volatility (with ±2 standard prediction errors) is very feasible since it possesses good convergence properties. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Augmented%20Dickey%20Fuller%20Test" title="Augmented Dickey Fuller Test">Augmented Dickey Fuller Test</a>, <a href="https://publications.waset.org/abstracts/search?q=geophysical%20time%20series" title=" geophysical time series"> geophysical time series</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20volatility%20model" title=" stochastic volatility model"> stochastic volatility model</a> </p> <a href="https://publications.waset.org/abstracts/75110/forecasting-the-volatility-of-geophysical-time-series-with-stochastic-volatility-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75110.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">315</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">928</span> Risk Propagation in Electricity Markets: Measuring the Asymmetric Transmission of Downside and Upside Risks in Energy Prices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Montserrat%20Guillen">Montserrat Guillen</a>, <a href="https://publications.waset.org/abstracts/search?q=Stephania%20Mosquera-Lopez"> Stephania Mosquera-Lopez</a>, <a href="https://publications.waset.org/abstracts/search?q=Jorge%20Uribe"> Jorge Uribe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An empirical study of market risk transmission between electricity prices in the Nord Pool interconnected market is done. Crucially, it is differentiated between risk propagation in the two tails of the price variation distribution. Thus, the downside risk from upside risk spillovers is distinguished. The results found document an asymmetric nature of risk and risk propagation in the two tails of the electricity price log variations. Risk spillovers following price increments in the market are transmitted to a larger extent than those after price reductions. Also, asymmetries related to both, the size of the transaction area and related to whether a given area behaves as a net-exporter or net-importer of electricity, are documented. For instance, on the one hand, the bigger the area of the transaction, the smaller the size of the volatility shocks that it receives. On the other hand, exporters of electricity, alongside countries with a significant dependence on renewable sources, tend to be net-transmitters of volatility to the rest of the system. Additionally, insights on the predictive power of positive and negative semivariances for future market volatility are provided. It is shown that depending on the forecasting horizon, downside and upside shocks to the market are featured by a distinctive persistence, and that upside volatility impacts more on net-importers of electricity, while the opposite holds for net-exporters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electricity%20prices" title="electricity prices">electricity prices</a>, <a href="https://publications.waset.org/abstracts/search?q=realized%20volatility" title=" realized volatility"> realized volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=semivariances" title=" semivariances"> semivariances</a>, <a href="https://publications.waset.org/abstracts/search?q=volatility%20spillovers" title=" volatility spillovers"> volatility spillovers</a> </p> <a href="https://publications.waset.org/abstracts/98350/risk-propagation-in-electricity-markets-measuring-the-asymmetric-transmission-of-downside-and-upside-risks-in-energy-prices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98350.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">175</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">927</span> Volatility Model with Markov Regime Switching to Forecast Baht/USD</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nop%20Sopipan">Nop Sopipan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we forecast the volatility of Baht/USDs using Markov Regime Switching GARCH (MRS-GARCH) models. These models allow volatility to have different dynamics according to unobserved regime variables. The main purpose of this paper is to find out whether MRS-GARCH models are an improvement on the GARCH type models in terms of modeling and forecasting Baht/USD volatility. The MRS-GARCH is the best performance model for Baht/USD volatility in short term but the GARCH model is best perform for long term. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=volatility" title="volatility">volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20Regime%20Switching" title=" Markov Regime Switching"> Markov Regime Switching</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=Baht%2FUSD" title=" Baht/USD"> Baht/USD</a> </p> <a href="https://publications.waset.org/abstracts/3942/volatility-model-with-markov-regime-switching-to-forecast-bahtusd" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3942.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">302</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">926</span> Earnings Volatility and Earnings Predictability</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yosra%20Ben%20Mhamed">Yosra Ben Mhamed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most previous research that investigates the importance of earnings volatility for a firm’s value has focused on the effects of earnings volatility on the cost of capital. Many study illustrate that earnings volatility can reduce the firm’s value by enhancing the cost of capital. However, a few recent studies directly examine the relation between earnings volatility and subsequent earnings levels. In our study, we further explore the role of volatility in forecasting. Our study makes two primary contributions to the literature. First, taking into account the level of current firm’s performance, we provide causal theory to the link between volatility and earnings predictability. Nevertheless, previous studies testing the linearity of this relationship have not mentioned any underlying theory. Secondly, our study contributes to the vast body of fundamental analysis research that identifies a set of variables that improve valuation, by showing that earnings volatility affects the estimation of future earnings. Projections of earnings are used by valuation research and practice to derive estimates of firm value. Since we want to examine the impact of volatility on earnings predictability, we sort the sample into three portfolios according to the level of their earnings volatility in ascending order. For each quintile, we present the predictability coefficient. In a second test, each of these portfolios is, then, sorted into three further quintiles based on their level of current earnings. These yield nine quintiles. So we can observe whether volatility strongly predicts decreases on earnings predictability only for highest quintile of earnings. In general, we find that earnings volatility has an inverse relationship with earnings predictability. Our results also show that the sensibility of earnings predictability to ex-ante volatility is more pronounced among profitability firms. The findings are most consistent with overinvestment and persistence explanations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=earnings%20volatility" title="earnings volatility">earnings volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=earnings%20predictability" title=" earnings predictability"> earnings predictability</a>, <a href="https://publications.waset.org/abstracts/search?q=earnings%20persistence" title=" earnings persistence"> earnings persistence</a>, <a href="https://publications.waset.org/abstracts/search?q=current%20profitability" title=" current profitability"> current profitability</a> </p> <a href="https://publications.waset.org/abstracts/23936/earnings-volatility-and-earnings-predictability" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23936.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">433</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">925</span> Combining the Dynamic Conditional Correlation and Range-GARCH Models to Improve Covariance Forecasts</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Piotr%20Fiszeder">Piotr Fiszeder</a>, <a href="https://publications.waset.org/abstracts/search?q=Marcin%20Fa%C5%82dzi%C5%84ski"> Marcin Fałdziński</a>, <a href="https://publications.waset.org/abstracts/search?q=Peter%20Moln%C3%A1r"> Peter Molnár</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The dynamic conditional correlation model of Engle (2002) is one of the most popular multivariate volatility models. However, this model is based solely on closing prices. It has been documented in the literature that the high and low price of the day can be used in an efficient volatility estimation. We, therefore, suggest a model which incorporates high and low prices into the dynamic conditional correlation framework. Empirical evaluation of this model is conducted on three datasets: currencies, stocks, and commodity exchange-traded funds. The utilisation of realized variances and covariances as proxies for true variances and covariances allows us to reach a strong conclusion that our model outperforms not only the standard dynamic conditional correlation model but also a competing range-based dynamic conditional correlation model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=volatility" title="volatility">volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=DCC%20model" title=" DCC model"> DCC model</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20and%20low%20prices" title=" high and low prices"> high and low prices</a>, <a href="https://publications.waset.org/abstracts/search?q=range-based%20models" title=" range-based models"> range-based models</a>, <a href="https://publications.waset.org/abstracts/search?q=covariance%20forecasting" title=" covariance forecasting"> covariance forecasting</a> </p> <a href="https://publications.waset.org/abstracts/107388/combining-the-dynamic-conditional-correlation-and-range-garch-models-to-improve-covariance-forecasts" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107388.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">183</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">924</span> The Impact of Exchange Rate Volatility on Real Total Export and Sub-Categories of Real Total Export of Malaysia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wong%20Hock%20Tsen">Wong Hock Tsen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aims to investigate the impact of exchange rate volatility on real export in Malaysia. The moving standard deviation with order three (MSD(3)) is used for the measurement of exchange rate volatility. The conventional and partially asymmetric autoregressive distributed lag (ARDL) models are used in the estimations. This study finds exchange rate volatility to have significant impact on real total export and some sub-categories of real total export. Moreover, this study finds that the positive or negative exchange rate volatility tends to have positive or negative impact on real export. Exchange rate volatility can be harmful to export of Malaysia. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exchange%20rate%20volatility" title="exchange rate volatility">exchange rate volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=autoregressive%20distributed%20lag" title=" autoregressive distributed lag"> autoregressive distributed lag</a>, <a href="https://publications.waset.org/abstracts/search?q=export" title=" export"> export</a>, <a href="https://publications.waset.org/abstracts/search?q=Malaysia" title=" Malaysia"> Malaysia</a> </p> <a href="https://publications.waset.org/abstracts/53891/the-impact-of-exchange-rate-volatility-on-real-total-export-and-sub-categories-of-real-total-export-of-malaysia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53891.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">324</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">923</span> Implied Adjusted Volatility by Leland Option Pricing Models: Evidence from Australian Index Options</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mimi%20Hafizah%20Abdullah">Mimi Hafizah Abdullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanani%20Farhah%20Harun"> Hanani Farhah Harun</a>, <a href="https://publications.waset.org/abstracts/search?q=Nik%20Ruzni%20Nik%20Idris"> Nik Ruzni Nik Idris</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the implied volatility as an important factor in financial decision-making, in particular in option pricing valuation, and also the given fact that the pricing biases of Leland option pricing models and the implied volatility structure for the options are related, this study considers examining the implied adjusted volatility smile patterns and term structures in the S&P/ASX 200 index options using the different Leland option pricing models. The examination of the implied adjusted volatility smiles and term structures in the Australian index options market covers the global financial crisis in the mid-2007. The implied adjusted volatility was found to escalate approximately triple the rate prior the crisis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=implied%20adjusted%20volatility" title="implied adjusted volatility">implied adjusted volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20crisis" title=" financial crisis"> financial crisis</a>, <a href="https://publications.waset.org/abstracts/search?q=Leland%20option%20pricing%20models" title=" Leland option pricing models"> Leland option pricing models</a>, <a href="https://publications.waset.org/abstracts/search?q=Australian%20index%20options" title=" Australian index options"> Australian index options</a> </p> <a href="https://publications.waset.org/abstracts/9747/implied-adjusted-volatility-by-leland-option-pricing-models-evidence-from-australian-index-options" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9747.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">379</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">922</span> Seeking Safe Haven: An Analysis of Gold Performance during Periods of High Volatility</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gerald%20Abdesaken">Gerald Abdesaken</a>, <a href="https://publications.waset.org/abstracts/search?q=Thomas%20O.%20Miller"> Thomas O. Miller</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper analyzes the performance of gold as a safe-haven investment. Assuming high market volatility as an impetus to seek a safe haven in gold, the return of gold relative to the stock market, as measured by the S&P 500, is tracked. Using the Chicago Board Options Exchange (CBOE) volatility index (VIX) as a measure of stock market volatility, various criteria are established for when an investor would seek a safe haven to avoid high levels of risk. The results show that in a vast majority of cases, the S&P 500 outperforms gold during these periods of high volatility and suggests investors who seek safe haven are underperforming the market. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gold" title="gold">gold</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20management" title=" portfolio management"> portfolio management</a>, <a href="https://publications.waset.org/abstracts/search?q=safe%20haven" title=" safe haven"> safe haven</a>, <a href="https://publications.waset.org/abstracts/search?q=VIX" title=" VIX"> VIX</a> </p> <a href="https://publications.waset.org/abstracts/137176/seeking-safe-haven-an-analysis-of-gold-performance-during-periods-of-high-volatility" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137176.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">163</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">921</span> Economic Growth: The Nexus of Oil Price Volatility and Renewable Energy Resources among Selected Developed and Developing Economies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Siddique">Muhammad Siddique</a>, <a href="https://publications.waset.org/abstracts/search?q=Volodymyr%20Lugovskyy"> Volodymyr Lugovskyy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper explores how nations might mitigate the unfavorable impacts of oil price volatility on economic growth by switching to renewable energy sources. The impacts of uncertain factor prices on economic activity are examined by looking at the Realized Volatility (RV) of oil prices rather than the more traditional method of looking at oil price shocks. The United States of America (USA), China (C), India (I), United Kingdom (UK), Germany (G), Malaysia (M), and Pakistan (P) are all included to round out the traditional literature's examination of selected nations, which focuses on oil-importing and exporting economies. Granger Causality Tests (GCT), Impulse Response Functions (IRF), and Variance Decompositions (VD) demonstrate that in a Vector Auto-Regressive (VAR) scenario, the negative impacts of oil price volatility extend beyond what can be explained by oil price shocks alone for all of the nations in the sample. Different nations have different levels of vulnerability to changes in oil prices and other factors that may play a role in a sectoral composition and the energy mix. The conventional method, which only takes into account whether a country is a net oil importer or exporter, is inadequate. The potential economic advantages of initiatives to decouple the macroeconomy from volatile commodities markets are shown through simulations of volatility shocks in alternative energy mixes (with greater proportions of renewables). It is determined that in developing countries like Pakistan, increasing the use of renewable energy sources might lessen an economy's sensitivity to changes in oil prices; nonetheless, a country-specific study is required to identify particular policy actions. In sum, the research provides an innovative justification for mitigating economic growth's dependence on stable oil prices in our sample countries. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=oil%20price%20volatility" title="oil price volatility">oil price volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=renewable%20energy" title=" renewable energy"> renewable energy</a>, <a href="https://publications.waset.org/abstracts/search?q=economic%20growth" title=" economic growth"> economic growth</a>, <a href="https://publications.waset.org/abstracts/search?q=developed%20and%20developing%20economies" title=" developed and developing economies"> developed and developing economies</a> </p> <a href="https://publications.waset.org/abstracts/164847/economic-growth-the-nexus-of-oil-price-volatility-and-renewable-energy-resources-among-selected-developed-and-developing-economies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164847.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">79</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">920</span> An Empirical Investigation of Uncertainty and the Lumpy Investment Channel of Monetary Policy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Min%20Fang">Min Fang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiaxi%20Yang"> Jiaxi Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Monetary policy could be less effective at stimulating investment during periods of elevated volatility than during normal times. In this paper, we argue that elevated volatility leads to a decrease in extensive margin investment incentive so that nominal stimulus generates less aggregate investment. To do this, we first empirically document that high volatility weakens firms’ investment responses to monetary stimulus. Such effects depend on the lumpiness nature of the firm-level investment. The findings are that the channel exists for all of the physical investment, innovation investment, and organization investment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=investment" title="investment">investment</a>, <a href="https://publications.waset.org/abstracts/search?q=irreversibility" title=" irreversibility"> irreversibility</a>, <a href="https://publications.waset.org/abstracts/search?q=volatility" title=" volatility"> volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=firm%20heterogeneity" title=" firm heterogeneity"> firm heterogeneity</a>, <a href="https://publications.waset.org/abstracts/search?q=monetary%20policy" title=" monetary policy"> monetary policy</a> </p> <a href="https://publications.waset.org/abstracts/162944/an-empirical-investigation-of-uncertainty-and-the-lumpy-investment-channel-of-monetary-policy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162944.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">106</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">919</span> Volatility Transmission between Oil Price and Stock Return of Emerging and Developed Countries</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Algia%20Hammami">Algia Hammami</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelfatteh%20Bouri"> Abdelfatteh Bouri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, our objective is to study the transmission of volatility between oil and stock markets in developed (USA, Germany, Italy, France and Japan) and emerging countries (Tunisia, Thailand, Brazil, Argentina, and Jordan) for the period 1998-2015. Our methodology consists of analyzing the monthly data by the GARCH-BEKK model to capture the effect in terms of volatility in the variation of the oil price on the different stock market. The empirical results in the emerging countries indicate that the relationships are unidirectional from the stock market to the oil market. For the developed countries, we find that the transmission of volatility is unidirectional from the oil market to stock market. For the USA and Italy, we find no transmission between the two markets. The transmission is bi-directional only in Thailand. Following our estimates, we also noticed that the emerging countries influence almost the same extent as the developed countries, while at the transmission of volatility there a bid difference. The GARCH-BEKK model is more effective than the others versions to minimize the risk of an oil-stock portfolio. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GARCH" title="GARCH">GARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=oil%20prices" title=" oil prices"> oil prices</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20market" title=" stock market"> stock market</a>, <a href="https://publications.waset.org/abstracts/search?q=volatility%20transmission" title=" volatility transmission"> volatility transmission</a> </p> <a href="https://publications.waset.org/abstracts/64379/volatility-transmission-between-oil-price-and-stock-return-of-emerging-and-developed-countries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64379.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">437</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">918</span> Portfolio Optimization under a Hybrid Stochastic Volatility and Constant Elasticity of Variance Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jai%20Heui%20Kim">Jai Heui Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Sotheara%20Veng"> Sotheara Veng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper studies the portfolio optimization problem for a pension fund under a hybrid model of stochastic volatility and constant elasticity of variance (CEV) using asymptotic analysis method. When the volatility component is fast mean-reverting, it is able to derive asymptotic approximations for the value function and the optimal strategy for general utility functions. Explicit solutions are given for the exponential and hyperbolic absolute risk aversion (HARA) utility functions. The study also shows that using the leading order optimal strategy results in the value function, not only up to the leading order, but also up to first order correction term. A practical strategy that does not depend on the unobservable volatility level is suggested. The result is an extension of the Merton's solution when stochastic volatility and elasticity of variance are considered simultaneously. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asymptotic%20analysis" title="asymptotic analysis">asymptotic analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=constant%20elasticity%20of%20variance" title=" constant elasticity of variance"> constant elasticity of variance</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20optimal%20control" title=" stochastic optimal control"> stochastic optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20volatility" title=" stochastic volatility"> stochastic volatility</a> </p> <a href="https://publications.waset.org/abstracts/50103/portfolio-optimization-under-a-hybrid-stochastic-volatility-and-constant-elasticity-of-variance-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50103.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">299</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">917</span> Structural Breaks, Asymmetric Effects and Long Memory in the Volatility of Turkey Stock Market </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serpil%20T%C3%BCrky%C4%B1lmaz">Serpil Türkyılmaz</a>, <a href="https://publications.waset.org/abstracts/search?q=Mesut%20Bal%C4%B1bey"> Mesut Balıbey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, long memory properties in volatility of Turkey Stock Market are being examined through the FIGARCH, FIEGARCH and FIAPARCH models under different distribution assumptions as normal and skewed student-t distributions. Furthermore, structural changes in volatility of Turkey Stock Market are investigated. The results display long memory property and the presence of asymmetric effects of shocks in volatility of Turkey Stock Market. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=FIAPARCH%20model" title="FIAPARCH model">FIAPARCH model</a>, <a href="https://publications.waset.org/abstracts/search?q=FIEGARCH%20model" title=" FIEGARCH model"> FIEGARCH model</a>, <a href="https://publications.waset.org/abstracts/search?q=FIGARCH%20model" title=" FIGARCH model"> FIGARCH model</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20break" title=" structural break"> structural break</a> </p> <a href="https://publications.waset.org/abstracts/14438/structural-breaks-asymmetric-effects-and-long-memory-in-the-volatility-of-turkey-stock-market" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14438.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">291</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">916</span> Efficient Frontier: Comparing Different Volatility Estimators</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tea%20Poklepovi%C4%87">Tea Poklepović</a>, <a href="https://publications.waset.org/abstracts/search?q=Zdravka%20Aljinovi%C4%87"> Zdravka Aljinović</a>, <a href="https://publications.waset.org/abstracts/search?q=Mario%20Matkovi%C4%87"> Mario Matković</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Modern Portfolio Theory (MPT) according to Markowitz states that investors form mean-variance efficient portfolios which maximizes their utility. Markowitz proposed the standard deviation as a simple measure for portfolio risk and the lower semi-variance as the only risk measure of interest to rational investors. This paper uses a third volatility estimator based on intraday data and compares three efficient frontiers on the Croatian Stock Market. The results show that range-based volatility estimator outperforms both mean-variance and lower semi-variance model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=variance" title="variance">variance</a>, <a href="https://publications.waset.org/abstracts/search?q=lower%20semi-variance" title=" lower semi-variance"> lower semi-variance</a>, <a href="https://publications.waset.org/abstracts/search?q=range-based%20volatility" title=" range-based volatility"> range-based volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=MPT" title=" MPT"> MPT</a> </p> <a href="https://publications.waset.org/abstracts/20229/efficient-frontier-comparing-different-volatility-estimators" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20229.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">513</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">915</span> Markov Switching of Conditional Variance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Josip%20Arneric">Josip Arneric</a>, <a href="https://publications.waset.org/abstracts/search?q=Blanka%20Skrabic%20Peric"> Blanka Skrabic Peric</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forecasting of volatility, i.e. returns fluctuations, has been a topic of interest to portfolio managers, option traders and market makers in order to get higher profits or less risky positions. Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most common used models are GARCH type models. As standard GARCH models show high volatility persistence, i.e. integrated behaviour of the conditional variance, it is difficult the predict volatility using standard GARCH models. Due to practical limitations of these models different approaches have been proposed in the literature, based on Markov switching models. In such situations models in which the parameters are allowed to change over time are more appropriate because they allow some part of the model to depend on the state of the economy. The empirical analysis demonstrates that Markov switching GARCH model resolves the problem of excessive persistence and outperforms uni-regime GARCH models in forecasting volatility for selected emerging markets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=emerging%20markets" title="emerging markets">emerging markets</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20switching" title=" Markov switching"> Markov switching</a>, <a href="https://publications.waset.org/abstracts/search?q=GARCH%20model" title=" GARCH model"> GARCH model</a>, <a href="https://publications.waset.org/abstracts/search?q=transition%20probabilities" title=" transition probabilities"> transition probabilities</a> </p> <a href="https://publications.waset.org/abstracts/23987/markov-switching-of-conditional-variance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23987.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">455</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">914</span> Volatility Switching between Two Regimes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Josip%20Viskovi%C4%87">Josip Visković</a>, <a href="https://publications.waset.org/abstracts/search?q=Josip%20Arneri%C4%87"> Josip Arnerić</a>, <a href="https://publications.waset.org/abstracts/search?q=Ante%20Rozga"> Ante Rozga</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most successful and popular models in modelling time varying volatility are GARCH type models. When financial returns exhibit sudden jumps that are due to structural breaks, standard GARCH models show high volatility persistence, i.e. integrated behaviour of the conditional variance. In such situations models in which the parameters are allowed to change over time are more appropriate. This paper compares different GARCH models in terms of their ability to describe structural changes in returns caused by financial crisis at stock markets of six selected central and east European countries. The empirical analysis demonstrates that Markov regime switching GARCH model resolves the problem of excessive persistence and outperforms uni-regime GARCH models in forecasting volatility when sudden switching occurs in response to financial crisis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=central%20and%20east%20European%20countries" title="central and east European countries">central and east European countries</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20crisis" title=" financial crisis"> financial crisis</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20switching%20GARCH%20model" title=" Markov switching GARCH model"> Markov switching GARCH model</a>, <a href="https://publications.waset.org/abstracts/search?q=transition%20probabilities" title=" transition probabilities"> transition probabilities</a> </p> <a href="https://publications.waset.org/abstracts/2227/volatility-switching-between-two-regimes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2227.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">226</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">913</span> Long- and Short-Term Impacts of COVID-19 and Gold Price on Price Volatility: A Comparative Study of MIDAS and GARCH-MIDAS Models for USA Crude Oil</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samir%20K.%20Safi">Samir K. Safi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this study was to compare the performance of two types of models, namely MIDAS and MIDAS-GARCH, in predicting the volatility of crude oil returns based on gold price returns and the COVID-19 pandemic. The study aimed to identify which model would provide more accurate short-term and long-term predictions and which model would perform better in handling the increased volatility caused by the pandemic. The findings of the study revealed that the MIDAS model performed better in predicting short-term and long-term volatility before the pandemic, while the MIDAS-GARCH model performed significantly better in handling the increased volatility caused by the pandemic. The study highlights the importance of selecting appropriate models to handle the complexities of real-world data and shows that the choice of model can significantly impact the accuracy of predictions. The practical implications of model selection and exploring potential methodological adjustments for future research will be highlighted and discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GARCH-MIDAS" title="GARCH-MIDAS">GARCH-MIDAS</a>, <a href="https://publications.waset.org/abstracts/search?q=MIDAS" title=" MIDAS"> MIDAS</a>, <a href="https://publications.waset.org/abstracts/search?q=crude%20oil" title=" crude oil"> crude oil</a>, <a href="https://publications.waset.org/abstracts/search?q=gold" title=" gold"> gold</a>, <a href="https://publications.waset.org/abstracts/search?q=COVID-19" title=" COVID-19"> COVID-19</a>, <a href="https://publications.waset.org/abstracts/search?q=volatility" title=" volatility"> volatility</a> </p> <a href="https://publications.waset.org/abstracts/184880/long-and-short-term-impacts-of-covid-19-and-gold-price-on-price-volatility-a-comparative-study-of-midas-and-garch-midas-models-for-usa-crude-oil" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184880.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">65</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">912</span> Co-integration for Soft Commodities with Non-Constant Volatility</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=E.%20Channol">E. Channol</a>, <a href="https://publications.waset.org/abstracts/search?q=O.%20Collet"> O. Collet</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Kostyuchyk"> N. Kostyuchyk</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Mesbah"> T. Mesbah</a>, <a href="https://publications.waset.org/abstracts/search?q=Quoc%20Hoang%20Long%20Nguyen"> Quoc Hoang Long Nguyen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a pricing model is proposed for co-integrated commodities extending Larsson model. The futures formulae have been derived and tests have been performed with non-constant volatility. The model has been applied to energy commodities (gas, CO2, energy) and soft commodities (corn, wheat). Results show that non-constant volatility leads to more accurate short term prices, which provides better evaluation of value-at-risk and more generally improve the risk management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=co-integration" title="co-integration">co-integration</a>, <a href="https://publications.waset.org/abstracts/search?q=soft%20commodities" title=" soft commodities"> soft commodities</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20management" title=" risk management"> risk management</a>, <a href="https://publications.waset.org/abstracts/search?q=value-at-risk" title=" value-at-risk"> value-at-risk</a> </p> <a href="https://publications.waset.org/abstracts/11078/co-integration-for-soft-commodities-with-non-constant-volatility" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11078.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">547</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">911</span> Numerical Simulation of Wishart Diffusion Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Raphael%20Naryongo">Raphael Naryongo</a>, <a href="https://publications.waset.org/abstracts/search?q=Philip%20%20Ngare"> Philip Ngare</a>, <a href="https://publications.waset.org/abstracts/search?q=Anthony%20%20Waititu"> Anthony Waititu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with numerical simulation of Wishart processes for a single asset risky pricing model whose volatility is described by Wishart affine diffusion processes. The multi-factor specification of volatility will make the model more flexible enough to fit the stock market data for short or long maturities for better returns. The Wishart process is a stochastic process which is a positive semi-definite matrix-valued generalization of the square root process. The aim of the study is to model the log asset stock returns under the double Wishart stochastic volatility model. The solution of the log-asset return dynamics for Bi-Wishart processes will be obtained through Euler-Maruyama discretization schemes. The numerical results on the asset returns are compared to the existing models returns such as Heston stochastic volatility model and double Heston stochastic volatility model <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=euler%20schemes" title="euler schemes">euler schemes</a>, <a href="https://publications.waset.org/abstracts/search?q=log-asset%20return" title=" log-asset return"> log-asset return</a>, <a href="https://publications.waset.org/abstracts/search?q=infinitesimal%20generator" title=" infinitesimal generator"> infinitesimal generator</a>, <a href="https://publications.waset.org/abstracts/search?q=wishart%20diffusion%20affine%20processes" title=" wishart diffusion affine processes "> wishart diffusion affine processes </a> </p> <a href="https://publications.waset.org/abstracts/137631/numerical-simulation-of-wishart-diffusion-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137631.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">378</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">910</span> Red Meat Price Volatility and Its' Relationship with Crude Oil and Exchange Rate </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Melek%20Akay">Melek Akay</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Turkey's agricultural commodity prices are prone to fluctuation but have gradually over time. A considerable amount of literature examines the changes in these prices by dealing with other commodities such as energy. Links between agricultural and energy markets have therefore been extensively investigated. Since red meat prices are becoming increasingly volatile in Turkey, this paper analyses the price volatility of veal, lamb and the relationship between red meat and crude oil, exchange rates by applying the generalize all period unconstraint volatility model, which generalises the GARCH (p, q) model for analysing weekly data covering a period of May 2006 to February 2017. Empirical results show that veal and lamb prices present volatility during the last decade, but particularly between 2009 and 2012. Moreover, oil prices have a significant effect on veal and lamb prices as well as their previous periods. Consequently, our research can lead policy makers to evaluate policy implementation in the appropriate way and reduce the impacts of oil prices by supporting producers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=red%20meat%20price" title="red meat price">red meat price</a>, <a href="https://publications.waset.org/abstracts/search?q=volatility" title=" volatility"> volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=crude%20oil" title=" crude oil"> crude oil</a>, <a href="https://publications.waset.org/abstracts/search?q=exchange%20rates" title=" exchange rates"> exchange rates</a>, <a href="https://publications.waset.org/abstracts/search?q=GARCH%20models" title=" GARCH models"> GARCH models</a>, <a href="https://publications.waset.org/abstracts/search?q=Turkey" title=" Turkey"> Turkey</a> </p> <a href="https://publications.waset.org/abstracts/118268/red-meat-price-volatility-and-its-relationship-with-crude-oil-and-exchange-rate" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118268.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">122</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">909</span> Leverage Effect for Volatility with Generalized Laplace Error</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Farrukh%20Javed">Farrukh Javed</a>, <a href="https://publications.waset.org/abstracts/search?q=Krzysztof%20Podg%C3%B3rski"> Krzysztof Podgórski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose a new model that accounts for the asymmetric response of volatility to positive ('good news') and negative ('bad news') shocks in economic time series the so-called leverage effect. In the past, asymmetric powers of errors in the conditionally heteroskedastic models have been used to capture this effect. Our model is using the gamma difference representation of the generalized Laplace distributions that efficiently models the asymmetry. It has one additional natural parameter, the shape, that is used instead of power in the asymmetric power models to capture the strength of a long-lasting effect of shocks. Some fundamental properties of the model are provided including the formula for covariances and an explicit form for the conditional distribution of 'bad' and 'good' news processes given the past the property that is important for the statistical fitting of the model. Relevant features of volatility models are illustrated using S&P 500 historical data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heavy%20tails" title="heavy tails">heavy tails</a>, <a href="https://publications.waset.org/abstracts/search?q=volatility%20clustering" title=" volatility clustering"> volatility clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20asymmetric%20laplace%20distribution" title=" generalized asymmetric laplace distribution"> generalized asymmetric laplace distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=leverage%20effect" title=" leverage effect"> leverage effect</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20heteroskedasticity" title=" conditional heteroskedasticity"> conditional heteroskedasticity</a>, <a href="https://publications.waset.org/abstracts/search?q=asymmetric%20power%20volatility" title=" asymmetric power volatility"> asymmetric power volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=GARCH%20models" title=" GARCH models "> GARCH models </a> </p> <a href="https://publications.waset.org/abstracts/18972/leverage-effect-for-volatility-with-generalized-laplace-error" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18972.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">385</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=realized%20volatility&page=2">2</a></li> <li class="page-item"><a class="page-link" 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