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garch</title> <meta name="description" content="Search results for: garch"> <meta name="keywords" content="garch"> <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 navbar-toggler ml-auto" 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class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="garch"> <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> 63</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: garch</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">63</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">62</span> ARIMA-GARCH, A Statistical Modeling for Epileptic Seizure Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salman%20Mohamadi">Salman Mohamadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Mohammad%20Ali%20Tayaranian%20Hosseini"> Seyed Mohammad Ali Tayaranian Hosseini</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamidreza%20Amindavar"> Hamidreza Amindavar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we provide a procedure to analyze and model EEG (electroencephalogram) signal as a time series using ARIMA-GARCH to predict an epileptic attack. The heteroskedasticity of EEG signal is examined through the ARCH or GARCH, (Autore- gressive conditional heteroskedasticity, Generalized autoregressive conditional heteroskedasticity) test. The best ARIMA-GARCH model in AIC sense is utilized to measure the volatility of the EEG from epileptic canine subjects, to forecast the future values of EEG. ARIMA-only model can perform prediction, but the ARCH or GARCH model acting on the residuals of ARIMA attains a con- siderable improved forecast horizon. First, we estimate the best ARIMA model, then different orders of ARCH and GARCH modelings are surveyed to determine the best heteroskedastic model of the residuals of the mentioned ARIMA. Using the simulated conditional variance of selected ARCH or GARCH model, we suggest the procedure to predict the oncoming seizures. The results indicate that GARCH modeling determines the dynamic changes of variance well before the onset of seizure. It can be inferred that the prediction capability comes from the ability of the combined ARIMA-GARCH modeling to cover the heteroskedastic nature of EEG signal changes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=epileptic%20seizure%20prediction" title="epileptic seizure prediction ">epileptic seizure prediction </a>, <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title=" ARIMA"> ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=ARCH%20and%20GARCH%20modeling" title=" ARCH and GARCH modeling"> ARCH and GARCH modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=heteroskedasticity" title=" heteroskedasticity"> heteroskedasticity</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG" title=" EEG"> EEG</a> </p> <a href="https://publications.waset.org/abstracts/59028/arima-garch-a-statistical-modeling-for-epileptic-seizure-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59028.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">406</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">61</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">60</span> Modelling the Dynamics of Corporate Bonds Spreads with Asymmetric GARCH Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S%C3%A9lima%20Baccar">Sélima Baccar</a>, <a href="https://publications.waset.org/abstracts/search?q=Ephraim%20Clark"> Ephraim Clark</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper can be considered as a new perspective to analyse credit spreads. A comprehensive empirical analysis of conditional variance of credit spreads indices is performed using various GARCH models. Based on a comparison between traditional and asymmetric GARCH models with alternative functional forms of the conditional density, we intend to identify what macroeconomic and financial factors have driven daily changes in the US Dollar credit spreads in the period from January 2011 through January 2013. The results provide a strong interdependence between credit spreads and the explanatory factors related to the conditions of interest rates, the state of the stock market, the bond market liquidity and the exchange risk. The empirical findings support the use of asymmetric GARCH models. The AGARCH and GJR models outperform the traditional GARCH in credit spreads modelling. We show, also, that the leptokurtic Student-t assumption is better than the Gaussian distribution and improves the quality of the estimates, whatever the rating or maturity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=corporate%20bonds" title="corporate bonds">corporate bonds</a>, <a href="https://publications.waset.org/abstracts/search?q=default%20risk" title=" default risk"> default risk</a>, <a href="https://publications.waset.org/abstracts/search?q=credit%20spreads" title=" credit spreads"> credit spreads</a>, <a href="https://publications.waset.org/abstracts/search?q=asymmetric%20garch%20models" title=" asymmetric garch models"> asymmetric garch models</a>, <a href="https://publications.waset.org/abstracts/search?q=student-t%20distribution" title=" student-t distribution"> student-t distribution</a> </p> <a href="https://publications.waset.org/abstracts/2699/modelling-the-dynamics-of-corporate-bonds-spreads-with-asymmetric-garch-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2699.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">474</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">59</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">58</span> A Comparative Study of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Extreme Value Theory (EVT) Model in Modeling Value-at-Risk (VaR)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Longqing%20Li">Longqing Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper addresses the inefficiency of the classical model in measuring the Value-at-Risk (VaR) using a normal distribution or a Student’s t distribution. Specifically, the paper focuses on the one day ahead Value-at-Risk (VaR) of major stock market’s daily returns in US, UK, China and Hong Kong in the most recent ten years under 95% confidence level. To improve the predictable power and search for the best performing model, the paper proposes using two leading alternatives, Extreme Value Theory (EVT) and a family of GARCH models, and compares the relative performance. The main contribution could be summarized in two aspects. First, the paper extends the GARCH family model by incorporating EGARCH and TGARCH to shed light on the difference between each in estimating one day ahead Value-at-Risk (VaR). Second, to account for the non-normality in the distribution of financial markets, the paper applies Generalized Error Distribution (GED), instead of the normal distribution, to govern the innovation term. A dynamic back-testing procedure is employed to assess the performance of each model, a family of GARCH and the conditional EVT. The conclusion is that Exponential GARCH yields the best estimate in out-of-sample one day ahead Value-at-Risk (VaR) forecasting. Moreover, the discrepancy of performance between the GARCH and the conditional EVT is indistinguishable. <p class="card-text"><strong>Keywords:</strong> <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=Extreme%20Value%20Theory" title=" Extreme Value Theory"> Extreme Value Theory</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20EVT" title=" conditional EVT"> conditional EVT</a>, <a href="https://publications.waset.org/abstracts/search?q=backtesting" title=" backtesting"> backtesting</a> </p> <a href="https://publications.waset.org/abstracts/49589/a-comparative-study-of-generalized-autoregressive-conditional-heteroskedasticity-garch-and-extreme-value-theory-evt-model-in-modeling-value-at-risk-var" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49589.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">321</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">57</span> Exchange Rate Forecasting by Econometric Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zahid%20Ahmad">Zahid Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Nosheen%20Imran"> Nosheen Imran</a>, <a href="https://publications.waset.org/abstracts/search?q=Nauman%20Ali"> Nauman Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Farah%20Amir"> Farah Amir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objective of the study is to forecast the US Dollar and Pak Rupee exchange rate by using time series models. For this purpose, daily exchange rates of US and Pakistan for the period of January 01, 2007 - June 2, 2017, are employed. The data set is divided into in sample and out of sample data set where in-sample data are used to estimate as well as forecast the models, whereas out-of-sample data set is exercised to forecast the exchange rate. The ADF test and PP test are used to make the time series stationary. To forecast the exchange rate ARIMA model and GARCH model are applied. Among the different Autoregressive Integrated Moving Average (ARIMA) models best model is selected on the basis of selection criteria. Due to the volatility clustering and ARCH effect the GARCH (1, 1) is also applied. Results of analysis showed that ARIMA (0, 1, 1 ) and GARCH (1, 1) are the most suitable models to forecast the future exchange rate. Further the GARCH (1,1) model provided the volatility with non-constant conditional variance in the exchange rate with good forecasting performance. This study is very useful for researchers, policymakers, and businesses for making decisions through accurate and timely forecasting of the exchange rate and helps them in devising their policies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exchange%20rate" title="exchange rate">exchange rate</a>, <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title=" ARIMA"> ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=GARCH" title=" GARCH"> GARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=PAK%2FUSD" title=" PAK/USD"> PAK/USD</a> </p> <a href="https://publications.waset.org/abstracts/75639/exchange-rate-forecasting-by-econometric-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75639.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">561</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">56</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">55</span> Estimating the Volatilite of Stock Markets in Case of Financial Crisis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gultekin%20Gurcay">Gultekin Gurcay</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, effects and responses of stock were analyzed. This analysis was done periodically. The dimensions of the financial crisis impact on the stock market were investigated by GARCH model. In this context, S&P 500 stock market is modeled with DAX, NIKKEI and BIST100. In this way, The effects of the changing in S&P 500 stock market were examined on European and Asian stock markets. Conditional variance coefficient will be calculated through garch model. The scope of the crisis period, the conditional covariance coefficient will be analyzed comparatively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conditional%20variance%20coefficient" title="conditional variance coefficient">conditional variance coefficient</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=garch%20model" title=" garch model"> garch model</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20market" title=" stock market"> stock market</a> </p> <a href="https://publications.waset.org/abstracts/40843/estimating-the-volatilite-of-stock-markets-in-case-of-financial-crisis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40843.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">294</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">54</span> Forecasting Electricity Spot Price with Generalized Long Memory Modeling: Wavelet and Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Souhir%20Ben%20Amor">Souhir Ben Amor</a>, <a href="https://publications.waset.org/abstracts/search?q=Heni%20Boubaker"> Heni Boubaker</a>, <a href="https://publications.waset.org/abstracts/search?q=Lotfi%20Belkacem"> Lotfi Belkacem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This aims of this paper is to forecast the electricity spot prices. First, we focus on modeling the conditional mean of the series so we adopt a generalized fractional -factor Gegenbauer process (k-factor GARMA). Secondly, the residual from the -factor GARMA model has used as a proxy for the conditional variance; these residuals were predicted using two different approaches. In the first approach, a local linear wavelet neural network model (LLWNN) has developed to predict the conditional variance using the Back Propagation learning algorithms. In the second approach, the Gegenbauer generalized autoregressive conditional heteroscedasticity process (G-GARCH) has adopted, and the parameters of the k-factor GARMA-G-GARCH model has estimated using the wavelet methodology based on the discrete wavelet packet transform (DWPT) approach. The empirical results have shown that the k-factor GARMA-G-GARCH model outperform the hybrid k-factor GARMA-LLWNN model, and find it is more appropriate for forecasts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electricity%20price" title="electricity price">electricity price</a>, <a href="https://publications.waset.org/abstracts/search?q=k-factor%20GARMA" title=" k-factor GARMA"> k-factor GARMA</a>, <a href="https://publications.waset.org/abstracts/search?q=LLWNN" title=" LLWNN"> LLWNN</a>, <a href="https://publications.waset.org/abstracts/search?q=G-GARCH" title=" G-GARCH"> G-GARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a> </p> <a href="https://publications.waset.org/abstracts/75361/forecasting-electricity-spot-price-with-generalized-long-memory-modeling-wavelet-and-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75361.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">231</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">53</span> On the Impact of Oil Price Fluctuations on Stock Markets: A Multivariate Long-Memory GARCH Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manel%20Youssef">Manel Youssef</a>, <a href="https://publications.waset.org/abstracts/search?q=Lotfi%20Belkacem"> Lotfi Belkacem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper employs multivariate long memory GARCH models to simultaneously estimate mean and conditional variance spillover effects between oil prices and different financial markets. Since different financial assets are traded based on these market sector returns, it’s important for financial market participants to understand the volatility transmission mechanism over time and across these series in order to make optimal portfolio allocation decisions. We examine weekly returns from January 1, 2003 to November 30, 2012 and find evidence of significant transmission of shocks and volatilities between oil prices and some of the examined financial markets. The findings support the idea of cross-market hedging and sharing of common information by investors. <p class="card-text"><strong>Keywords:</strong> <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%20indices%20returns" title=" stock indices returns"> stock indices returns</a>, <a href="https://publications.waset.org/abstracts/search?q=oil%20volatility" title=" oil volatility"> oil volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=contagion" title=" contagion"> contagion</a>, <a href="https://publications.waset.org/abstracts/search?q=DCC-multivariate%20%28FI%29%20GARCH" title=" DCC-multivariate (FI) GARCH"> DCC-multivariate (FI) GARCH</a> </p> <a href="https://publications.waset.org/abstracts/20756/on-the-impact-of-oil-price-fluctuations-on-stock-markets-a-multivariate-long-memory-garch-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20756.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">533</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">52</span> Modelling Agricultural Commodity Price Volatility with Markov-Switching Regression, Single Regime GARCH and Markov-Switching GARCH Models: Empirical Evidence from South Africa</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yegnanew%20A.%20Shiferaw">Yegnanew A. Shiferaw</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: commodity price volatility originating from excessive commodity price fluctuation has been a global problem especially after the recent financial crises. Volatility is a measure of risk or uncertainty in financial analysis. It plays a vital role in risk management, portfolio management, and pricing equity. Objectives: the core objective of this paper is to examine the relationship between the prices of agricultural commodities with oil price, gas price, coal price and exchange rate (USD/Rand). In addition, the paper tries to fit an appropriate model that best describes the log return price volatility and estimate Value-at-Risk and expected shortfall. Data and methods: the data used in this study are the daily returns of agricultural commodity prices from 02 January 2007 to 31st October 2016. The data sets consists of the daily returns of agricultural commodity prices namely: white maize, yellow maize, wheat, sunflower, soya, corn, and sorghum. The paper applies the three-state Markov-switching (MS) regression, the standard single-regime GARCH and the two regime Markov-switching GARCH (MS-GARCH) models. Results: to choose the best fit model, the log-likelihood function, Akaike information criterion (AIC), Bayesian information criterion (BIC) and deviance information criterion (DIC) are employed under three distributions for innovations. The results indicate that: (i) the price of agricultural commodities was found to be significantly associated with the price of coal, price of natural gas, price of oil and exchange rate, (ii) for all agricultural commodities except sunflower, k=3 had higher log-likelihood values and lower AIC and BIC values. Thus, the three-state MS regression model outperformed the two-state MS regression model (iii) MS-GARCH(1,1) with generalized error distribution (ged) innovation performs best for white maize and yellow maize; MS-GARCH(1,1) with student-t distribution (std) innovation performs better for sorghum; MS-gjrGARCH(1,1) with ged innovation performs better for wheat, sunflower and soya and MS-GARCH(1,1) with std innovation performs better for corn. In conclusion, this paper provided a practical guide for modelling agricultural commodity prices by MS regression and MS-GARCH processes. This paper can be good as a reference when facing modelling agricultural commodity price problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=commodity%20prices" title="commodity prices">commodity prices</a>, <a href="https://publications.waset.org/abstracts/search?q=MS-GARCH%20model" title=" MS-GARCH model"> MS-GARCH model</a>, <a href="https://publications.waset.org/abstracts/search?q=MS%20regression%20model" title=" MS regression model"> MS regression model</a>, <a href="https://publications.waset.org/abstracts/search?q=South%20Africa" title=" South Africa"> South Africa</a>, <a href="https://publications.waset.org/abstracts/search?q=volatility" title=" volatility"> volatility</a> </p> <a href="https://publications.waset.org/abstracts/80554/modelling-agricultural-commodity-price-volatility-with-markov-switching-regression-single-regime-garch-and-markov-switching-garch-models-empirical-evidence-from-south-africa" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80554.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">202</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">51</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">50</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">49</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">48</span> Designing Price Stability Model of Red Cayenne Pepper Price in Wonogiri District, Centre Java, Using ARCH/GARCH Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fauzia%20Dianawati">Fauzia Dianawati</a>, <a href="https://publications.waset.org/abstracts/search?q=Riska%20W.%20Purnomo"> Riska W. Purnomo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Food and agricultural sector become the biggest sector contributing to inflation in Indonesia. Especially in Wonogiri district, red cayenne pepper was the biggest sector contributing to inflation on 2016. A national statistic proved that in recent five years red cayenne pepper has the highest average level of fluctuation among all commodities. Some factors, like supply chain, price disparity, production quantity, crop failure, and oil price become the possible factor causes high volatility level in red cayenne pepper price. Therefore, this research tries to find the key factor causing fluctuation on red cayenne pepper by using ARCH/GARCH method. The method could accommodate the presence of heteroscedasticity in time series data. At the end of the research, it is statistically found that the second level of supply chain becomes the biggest part contributing to inflation with 3,35 of coefficient in fluctuation forecasting model of red cayenne pepper price. This model could become a reference to the government to determine the appropriate policy in maintaining the price stability of red cayenne pepper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARCH%2FGARCH" title="ARCH/GARCH">ARCH/GARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=red%20cayenne%20pepper" title=" red cayenne pepper"> red cayenne pepper</a>, <a href="https://publications.waset.org/abstracts/search?q=volatility" title=" volatility"> volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20chain" title=" supply chain"> supply chain</a> </p> <a href="https://publications.waset.org/abstracts/79137/designing-price-stability-model-of-red-cayenne-pepper-price-in-wonogiri-district-centre-java-using-archgarch-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79137.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">186</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">47</span> Modelling Volatility of Cryptocurrencies: Evidence from GARCH Family of Models with Skewed Error Innovation Distributions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Timothy%20Kayode%20Samson">Timothy Kayode Samson</a>, <a href="https://publications.waset.org/abstracts/search?q=Adedoyin%20Isola%20Lawal"> Adedoyin Isola Lawal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The past five years have shown a sharp increase in public interest in the crypto market, with its market capitalization growing from $100 billion in June 2017 to $2158.42 billion on April 5, 2022. Despite the outrageous nature of the volatility of cryptocurrencies, the use of skewed error innovation distributions in modelling the volatility behaviour of these digital currencies has not been given much research attention. Hence, this study models the volatility of 5 largest cryptocurrencies by market capitalization (Bitcoin, Ethereum, Tether, Binance coin, and USD Coin) using four variants of GARCH models (GJR-GARCH, sGARCH, EGARCH, and APARCH) estimated using three skewed error innovation distributions (skewed normal, skewed student- t and skewed generalized error innovation distributions). Daily closing prices of these currencies were obtained from Yahoo Finance website. Finding reveals that the Binance coin reported higher mean returns compared to other digital currencies, while the skewness indicates that the Binance coin, Tether, and USD coin increased more than they decreased in values within the period of study. For both Bitcoin and Ethereum, negative skewness was obtained, meaning that within the period of study, the returns of these currencies decreased more than they increased in value. Returns from these cryptocurrencies were found to be stationary but not normality distributed with evidence of the ARCH effect. The skewness parameters in all best forecasting models were all significant (p<.05), justifying of use of skewed error innovation distributions with a fatter tail than normal, Student-t, and generalized error innovation distributions. For Binance coin, EGARCH-sstd outperformed other volatility models, while for Bitcoin, Ethereum, Tether, and USD coin, the best forecasting models were EGARCH-sstd, APARCH-sstd, EGARCH-sged, and GJR-GARCH-sstd, respectively. This suggests the superiority of skewed Student t- distribution and skewed generalized error distribution over the skewed normal distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=skewed%20generalized%20error%20distribution" title="skewed generalized error distribution">skewed generalized error distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=skewed%20normal%20distribution" title=" skewed normal distribution"> skewed normal distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=skewed%20student%20t-%20distribution" title=" skewed student t- distribution"> skewed student t- distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=APARCH" title=" APARCH"> APARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=EGARCH" title=" EGARCH"> EGARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=sGARCH" title=" sGARCH"> sGARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=GJR-GARCH" title=" GJR-GARCH"> GJR-GARCH</a> </p> <a href="https://publications.waset.org/abstracts/151699/modelling-volatility-of-cryptocurrencies-evidence-from-garch-family-of-models-with-skewed-error-innovation-distributions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151699.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">119</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">46</span> The Effect of Recycling on Price Volatility of Critical Metals in the EU (2010-2019): An Application of Multivariate GARCH Family Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marc%20Evenst%20Jn%20Jacques">Marc Evenst Jn Jacques</a>, <a href="https://publications.waset.org/abstracts/search?q=Sophie%20Bernard"> Sophie Bernard</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electrical and electronic applications, as well as rechargeable batteries, are common in any economy. They also contain a number of important and valuable metals. It is critical to investigate the impact of these new materials or volume sources on the metal market dynamics. This paper investigates the impact of responsible recycling within the European region on metal price volatility. As far as we know, no empirical studies have been conducted to assess the role of metal recycling in metal market price volatility. The goal of this paper is to test the claim that metal recycling helps to cushion price volatility. A set of circular economy indicators/variables, namely, 1) annual total trade values of recycled metals, 2) annual volume of scrap traded and 3) circular material use rate, and 4) information about recycling, are used to estimate the volatility of monthly spot prices of regular metals. A combination of the GARCH-MIDAS model for mixed frequency data sampling and a simple GARCH (1,1) model for the same frequency variables was adopted to examine the potential links between each variable and price volatility. We discovered that from 2010 to 2019, except for Nickel, scrap consumption (Millions of tons), Scrap Trade Values, and Recycled Material use rate had no significant impact on the price volatility of standard metals (Aluminum, Lead) and precious metals (Gold and Platinum). Worldwide interest in recycling has no impact on returns or volatility. Specific interest in metal recycling did have a link to the mean return equation for Aluminum, Gold and to the volatility equation for lead and Nickel. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=recycling" title="recycling">recycling</a>, <a href="https://publications.waset.org/abstracts/search?q=circular%20economy" title=" circular economy"> circular economy</a>, <a href="https://publications.waset.org/abstracts/search?q=price%20volatility" title=" price volatility"> price volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=GARCH" title=" GARCH"> GARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20data%20sampling" title=" mixed data sampling"> mixed data sampling</a> </p> <a href="https://publications.waset.org/abstracts/160352/the-effect-of-recycling-on-price-volatility-of-critical-metals-in-the-eu-2010-2019-an-application-of-multivariate-garch-family-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160352.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">57</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">45</span> The Effect of Oil Price Uncertainty on Food Price in South Africa</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Goodness%20C.%20Aye">Goodness C. Aye</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper examines the effect of the volatility of oil prices on food price in South Africa using monthly data covering the period 2002:01 to 2014:09. Food price is measured by the South African consumer price index for food while oil price is proxied by the Brent crude oil. The study employs the GARCH-in-mean VAR model, which allows the investigation of the effect of a negative and positive shock in oil price volatility on food price. The model also allows the oil price uncertainty to be measured as the conditional standard deviation of a one-step-ahead forecast error of the change in oil price. The results show that oil price uncertainty has a positive and significant effect on food price in South Africa. The responses of food price to a positive and negative oil price shocks is asymmetric. <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=food%20price" title=" food price"> food price</a>, <a href="https://publications.waset.org/abstracts/search?q=bivariate" title=" bivariate"> bivariate</a>, <a href="https://publications.waset.org/abstracts/search?q=GARCH-in-mean%20VAR" title=" GARCH-in-mean VAR"> GARCH-in-mean VAR</a>, <a href="https://publications.waset.org/abstracts/search?q=asymmetric" title=" asymmetric"> asymmetric</a> </p> <a href="https://publications.waset.org/abstracts/28399/the-effect-of-oil-price-uncertainty-on-food-price-in-south-africa" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28399.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">477</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">44</span> The Impact of the Global Financial Crises on MILA Stock Markets </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Miriam%20Sosa">Miriam Sosa</a>, <a href="https://publications.waset.org/abstracts/search?q=Edgar%20Ortiz"> Edgar Ortiz</a>, <a href="https://publications.waset.org/abstracts/search?q=Alejandra%20Cabello"> Alejandra Cabello</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper examines the volatility changes and leverage effects of the MILA stock markets and their changes since the 2007 global financial crisis. This group integrates the stock markets from Chile, Colombia, Mexico and Peru. Volatility changes and leverage effects are tested with a symmetric GARCH (1,1) and asymmetric TARCH (1,1) models with a dummy variable in the variance equation. Daily closing prices of the stock indexes of Chile (IPSA), Colombia (COLCAP), Mexico (IPC) and Peru (IGBVL) are examined for the period 2003:01 to 2015:02. The evidence confirms the presence of an overall increase in asymmetric market volatility in the Peruvian share market since the 2007 crisis. <p class="card-text"><strong>Keywords:</strong> <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=Latin%20American%20Integrated%20Market" title=" Latin American Integrated Market"> Latin American Integrated Market</a>, <a href="https://publications.waset.org/abstracts/search?q=TARCH" title=" TARCH"> TARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=GARCH" title=" GARCH"> GARCH</a> </p> <a href="https://publications.waset.org/abstracts/57884/the-impact-of-the-global-financial-crises-on-mila-stock-markets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57884.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">279</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">43</span> Volatility Spillover and Hedging Effectiveness between Gold and Stock Markets: Evidence for BRICS Countries</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Walid%20Chkili">Walid Chkili</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper investigates the dynamic relationship between gold and stock markets using data for BRICS counties. For this purpose, we estimate three multivariate GARCH models (namely CCC, DCC and BEKK) for weekly stock and gold data. Our main objective is to examine time variations in conditional correlations between the two assets and to check the effectiveness use of gold as a hedge for equity markets. Empirical results reveal that dynamic conditional correlations switch between positive and negative values over the period under study. This correlation is negative during the major financial crises suggesting that gold can act as a safe haven during the major stress period of stock markets. We also evaluate the implications for portfolio diversification and hedging effectiveness for the pair gold/stock. Our findings suggest that adding gold in the stock portfolio enhance its risk-adjusted return. <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=financial%20markets" title=" financial markets"> financial markets</a>, <a href="https://publications.waset.org/abstracts/search?q=hedge" title=" hedge"> hedge</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20GARCH" title=" multivariate GARCH"> multivariate GARCH</a> </p> <a href="https://publications.waset.org/abstracts/20064/volatility-spillover-and-hedging-effectiveness-between-gold-and-stock-markets-evidence-for-brics-countries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20064.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">472</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">42</span> Volatility of Interest Rates in the US After Covid-19: A Multivariate GARCH Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rodrigo%20Baggi%20Prieto%20Alvarez">Rodrigo Baggi Prieto Alvarez</a>, <a href="https://publications.waset.org/abstracts/search?q=Jos%C3%A9%20Dias%20Curto"> José Dias Curto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study examines the volatility dynamics of U.S. Treasury rates from 1994 to 2024, with a focus on the shock induced by the Covid-19 pandemic. This market is considered the most important to monitor daily, as the yield curve of future interest rates is often referred to as "the mother of all curves" due to its importance in the pricing of all global risk assets. The period after 2020 was characterized initially by a stimulative monetary policy, synchronized across major global economies, with a rapid and significant reduction of interest rates by central banks and expansionary fiscal policy and increased government debt. In a subsequent phase, from 2021 to 2022, the end of lockdowns, the boost in income through public subsidies, and increased demand for goods, combined with logistical bottlenecks, resulted in the most significant inflationary shock in decades. The Federal Reserve (Fed) employed an abrupt tightening, raising short-term interest rates from 0.00% to 5.25% p.a. (the highest since the 2000s) at record speed (March 2022 to July 2023), and even before the monetary tightening, long-term interest rates had already been on an upward trend since 2020. The speed at which the Fed raised short-term interest rates has a significant impact on the level and the volatility of yields across other maturities. Estimating models as APARCH and DCC-GARCH, this paper explores the interplay between conditional variance in the 2-year Treasuries and key macroeconomic variables for the U.S., highlighting asymmetric shocks, feedback effects, and spillovers between Treasury markets and macroeconomic volatility. The results evidenced volatility peaks, particularly during the Covid-19 lockdown, and the statistical tests confirmed ARCH/GARCH effects, corroborating high persistence, i.e. future variance being strongly affected by past variance. The univariate models GJR-GARCH and APARCH allowed to verify the importance of asymmetry, that is, bad news have a greater impact than good news on the conditional volatility of future interest rates. Then, the multivariate DCC-GARCH model confirmed the spillover between the volatility of Treasuries and volatility of macroeconomic variables, indicating the time-varying conditional correlation between the variable’s volatilities. Besides estimating a full specification for DCC-GARCH with all variables simultaneously, a robustness test with pairwise estimations confirmed the temporal dynamics of highly persistence volatility and corroborated the feedback effect between the 2-year Treasuries, the unemployment rate and expected inflation, suggesting that these variables are good predictors of the long-term interest rate, which is aligned with the Fed's dual mandate. The empirical results here are consistent with the literature and bring practical insights for risk management and investment strategies, supporting investors to better model asymmetry and downside risk in portfolios and to manage the interest rate risk by understanding how different maturities respond to economic conditions. <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=US%20treasury" title=" US treasury"> US treasury</a>, <a href="https://publications.waset.org/abstracts/search?q=APARCH" title=" APARCH"> APARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=DCC-GARCH" title=" DCC-GARCH"> DCC-GARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=asymmetric%20shocks" title=" asymmetric shocks"> asymmetric shocks</a>, <a href="https://publications.waset.org/abstracts/search?q=spillover" title=" spillover"> spillover</a> </p> <a href="https://publications.waset.org/abstracts/195404/volatility-of-interest-rates-in-the-us-after-covid-19-a-multivariate-garch-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/195404.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">0</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">41</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">40</span> Heat Waves Effect on Stock Return and Volatility: Evidence from Stock Market and Selected Industries in Pakistan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sayed%20Kifayat%20Shah">Sayed Kifayat Shah</a>, <a href="https://publications.waset.org/abstracts/search?q=Tang%20Zhongjun"> Tang Zhongjun</a>, <a href="https://publications.waset.org/abstracts/search?q=Arfa%20Tanveer"> Arfa Tanveer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study explores the significant heatwave effect on stock return and volatility. Using an ARCH/GARCH approach, it examines the relationship between the heatwave of Karachi, Islamabad, and Lahore on the KSE-100 index. It also explores the impact of heatwave on returns of the pharmaceutical and electronics industries. The empirical results confirm that that stock return is positively related to the heat waves of Karachi, negatively related to that of Islamabad, and is not affected by the heatwave of Lahore. Similarly, pharmaceutical and electronics indices are also positively related to heatwaves. These differences in results can be ascribed to the change in the behavior of the residents of that city. The outcomes are useful for understanding an investor's behavior reacting to weather and fluxes in stock price related to heatwave severity levels. The results can support investors in fixing biases in behavior. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARCH%2FGARCH%20model" title="ARCH/GARCH model">ARCH/GARCH model</a>, <a href="https://publications.waset.org/abstracts/search?q=heat%20wave" title=" heat wave"> heat wave</a>, <a href="https://publications.waset.org/abstracts/search?q=KSE-100%20index" title=" KSE-100 index"> KSE-100 index</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20market%20return" title=" stock market return"> stock market return</a> </p> <a href="https://publications.waset.org/abstracts/129970/heat-waves-effect-on-stock-return-and-volatility-evidence-from-stock-market-and-selected-industries-in-pakistan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129970.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">156</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">39</span> Day of the Week Patterns and the Financial Trends' Role: Evidence from the Greek Stock Market during the Euro Era</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nikolaos%20Konstantopoulos">Nikolaos Konstantopoulos</a>, <a href="https://publications.waset.org/abstracts/search?q=Aristeidis%20Samitas"> Aristeidis Samitas</a>, <a href="https://publications.waset.org/abstracts/search?q=Vasileiou%20Evangelos"> Vasileiou Evangelos </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this study is to examine if the financial trends influence not only the stock markets’ returns, but also their anomalies. We choose to study the day of the week effect (DOW) for the Greek stock market during the Euro period (2002-12), because during the specific period there are not significant structural changes and there are long term financial trends. Moreover, in order to avoid possible methodological counterarguments that usually arise in the literature, we apply several linear (OLS) and nonlinear (GARCH family) models to our sample until we reach to the conclusion that the TGARCH model fits better to our sample than any other. Our results suggest that in the Greek stock market there is a long term predisposition for positive/negative returns depending on the weekday. However, the statistical significance is influenced from the financial trend. This influence may be the reason why there are conflict findings in the literature through the time. Finally, we combine the DOW’s empirical findings from 1985-2012 and we may assume that in the Greek case there is a tendency for long lived turn of the week effect. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=day%20of%20the%20week%20effect" title="day of the week effect">day of the week effect</a>, <a href="https://publications.waset.org/abstracts/search?q=GARCH%20family%20models" title=" GARCH family models"> GARCH family models</a>, <a href="https://publications.waset.org/abstracts/search?q=Athens%20stock%20exchange" title=" Athens stock exchange"> Athens stock exchange</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=crisis" title=" crisis"> crisis</a> </p> <a href="https://publications.waset.org/abstracts/6926/day-of-the-week-patterns-and-the-financial-trends-role-evidence-from-the-greek-stock-market-during-the-euro-era" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6926.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">410</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">38</span> Dynamic Correlations and Portfolio Optimization between Islamic and Conventional Equity Indexes: A Vine Copula-Based Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Imen%20Dhaou">Imen Dhaou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study examines conditional Value at Risk by applying the GJR-EVT-Copula model, and finds the optimal portfolio for eight Dow Jones Islamic-conventional pairs. Our methodology consists of modeling the data by a bivariate GJR-GARCH model in which we extract the filtered residuals and then apply the Peak over threshold model (POT) to fit the residual tails in order to model marginal distributions. After that, we use pair-copula to find the optimal portfolio risk dependence structure. Finally, with Monte Carlo simulations, we estimate the Value at Risk (VaR) and the conditional Value at Risk (CVaR). The empirical results show the VaR and CVaR values for an equally weighted portfolio of Dow Jones Islamic-conventional pairs. In sum, we found that the optimal investment focuses on Islamic-conventional US Market index pairs because of high investment proportion; however, all other index pairs have low investment proportion. These results deliver some real repercussions for portfolio managers and policymakers concerning to optimal asset allocations, portfolio risk management and the diversification advantages of these markets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CVaR" title="CVaR">CVaR</a>, <a href="https://publications.waset.org/abstracts/search?q=Dow%20Jones%20Islamic%20index" title=" Dow Jones Islamic index"> Dow Jones Islamic index</a>, <a href="https://publications.waset.org/abstracts/search?q=GJR-GARCH-EVT-pair%20copula" title=" GJR-GARCH-EVT-pair copula"> GJR-GARCH-EVT-pair copula</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a> </p> <a href="https://publications.waset.org/abstracts/81937/dynamic-correlations-and-portfolio-optimization-between-islamic-and-conventional-equity-indexes-a-vine-copula-based-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81937.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">256</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">37</span> Financial Centers and BRICS Stock Markets: The Effect of the Recent Crises</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marco%20Barassi">Marco Barassi</a>, <a href="https://publications.waset.org/abstracts/search?q=Nicola%20Spagnolo"> Nicola Spagnolo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper uses a DCC-GARCH model framework to examine mean and volatility spillovers (i.e. causality in mean and variance) dynamics between financial centers and the stock market indexes of the BRICS countries. In addition, tests for changes in the transmission mechanism are carried out by first testing for structural breaks and then setting a dummy variable to control for the 2008 financial crises. We use weekly data for nine countries, four financial centers (Germany, Japan, UK and USA) and the five BRICS countries (Brazil, Russia, India, China and South Africa). Furthermore, we control for monetary policy using domestic interest rates (90-day Treasury Bill interest rate) over the period 03/1/1990 - 04/2/2014, for a total of 1204 observations. Results show that the 2008 financial crises changed the causality dynamics for most of the countries considered. The same pattern can also be observed in conditional correlation showing a shift upward following the turbulence associated to the 2008 crises. The magnitude of these effects suggests a leading role played by the financial centers in effecting Brazil and South Africa, whereas Russia, India and China show a higher degree of resilience. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=financial%20crises" title="financial crises">financial crises</a>, <a href="https://publications.waset.org/abstracts/search?q=DCC-GARCH%20model" title=" DCC-GARCH model"> DCC-GARCH model</a>, <a href="https://publications.waset.org/abstracts/search?q=volatility%20spillovers" title=" volatility spillovers"> volatility spillovers</a>, <a href="https://publications.waset.org/abstracts/search?q=economics" title=" economics"> economics</a> </p> <a href="https://publications.waset.org/abstracts/8104/financial-centers-and-brics-stock-markets-the-effect-of-the-recent-crises" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8104.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">357</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">36</span> Modelling Impacts of Global Financial Crises on Stock Volatility of Nigeria Banks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maruf%20Ariyo%20Raheem">Maruf Ariyo Raheem</a>, <a href="https://publications.waset.org/abstracts/search?q=Patrick%20Oseloka%20Ezepue"> Patrick Oseloka Ezepue</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research aimed at determining most appropriate heteroskedastic model to predicting volatility of 10 major Nigerian banks: Access, United Bank for Africa (UBA), Guaranty Trust, Skye, Diamond, Fidelity, Sterling, Union, ETI and Zenith banks using daily closing stock prices of each of the banks from 2004 to 2014. The models employed include ARCH (1), GARCH (1, 1), EGARCH (1, 1) and TARCH (1, 1). The results show that all the banks returns are highly leptokurtic, significantly skewed and thus non-normal across the four periods except for Fidelity bank during financial crises; findings similar to those of other global markets. There is also strong evidence for the presence of heteroscedasticity, and that volatility persistence during crisis is higher than before the crisis across the 10 banks, with that of UBA taking the lead, about 11 times higher during the crisis. Findings further revealed that Asymmetric GARCH models became dominant especially during financial crises and post crises when the second reforms were introduced into the banking industry by the Central Bank of Nigeria (CBN). Generally, one could say that Nigerian banks returns are volatility persistent during and after the crises, and characterised by leverage effects of negative and positive shocks during these periods <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=global%20financial%20crisis" title="global financial crisis">global financial crisis</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=persistence" title=" persistence"> persistence</a>, <a href="https://publications.waset.org/abstracts/search?q=volatility%20clustering" title=" volatility clustering"> volatility clustering</a> </p> <a href="https://publications.waset.org/abstracts/49896/modelling-impacts-of-global-financial-crises-on-stock-volatility-of-nigeria-banks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49896.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">526</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">35</span> Determinants of International Volatility Passthroughs of Agricultural Commodities: A Panel Analysis of Developing Countries</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tetsuji%20Tanaka">Tetsuji Tanaka</a>, <a href="https://publications.waset.org/abstracts/search?q=Jin%20Guo"> Jin Guo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The extant literature has not succeeded in uncovering the common determinants of price volatility transmissions of agricultural commodities from international to local markets, and further, has rarely investigated the role of self-sufficiency measures in the context of national food security. We analyzed various factors to determine the degree of price volatility transmissions of wheat, rice, and maize between world and domestic markets using GARCH models with dynamic conditional correlation (DCC) specifications and panel-feasible generalized least square models. We found that the grain autarky system has the potential to diminish volatility pass-throughs for three grain commodities. Furthermore, it was discovered that the substitutive commodity consumption behavior between maize and wheat buffers the volatility transmissions of both, but rice does not function as a transmission-relieving element, either for the volatilities of wheat or maize. The effectiveness of grain consumption substitution to insulate the pass-throughs from global markets is greater than that of cereal self-sufficiency. These implications are extremely beneficial for developing governments to protect their domestic food markets from uncertainty in foreign countries and as such, improves food security. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=food%20security" title="food security">food security</a>, <a href="https://publications.waset.org/abstracts/search?q=GARCH" title=" GARCH"> GARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=grain%20self-sufficiency" title=" grain self-sufficiency"> grain self-sufficiency</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/101522/determinants-of-international-volatility-passthroughs-of-agricultural-commodities-a-panel-analysis-of-developing-countries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101522.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">34</span> Downside Risk Analysis of the Nigerian Stock Market: A Value at Risk Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Godwin%20Chigozie%20Okpara">Godwin Chigozie Okpara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper using standard GARCH, EGARCH, and TARCH models on day of the week return series (of 246 days) from the Nigerian Stock market estimated the model variants’ VaR. An asymmetric return distribution and fat-tail phenomenon in financial time series were considered by estimating the models with normal, student t and generalized error distributions. The analysis based on Akaike Information Criterion suggests that the EGARCH model with student t innovation distribution can furnish more accurate estimate of VaR. In the light of this, we apply the likelihood ratio tests of proportional failure rates to VaR derived from EGARCH model in order to determine the short and long positions VaR performances. The result shows that as alpha ranges from 0.05 to 0.005 for short positions, the failure rate significantly exceeds the prescribed quintiles while it however shows no significant difference between the failure rate and the prescribed quantiles for long positions. This suggests that investors and portfolio managers in the Nigeria stock market have long trading position or can buy assets with concern on when the asset prices will fall. Precisely, the VaR estimates for the long position range from -4.7% for 95 percent confidence level to -10.3% for 99.5 percent confidence level. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=downside%20risk" title="downside risk">downside risk</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=failure%20rate" title=" failure rate"> failure rate</a>, <a href="https://publications.waset.org/abstracts/search?q=kupiec%20LR%20tests" title=" kupiec LR tests"> kupiec LR tests</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/21471/downside-risk-analysis-of-the-nigerian-stock-market-a-value-at-risk-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21471.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">443</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=garch&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=garch&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=garch&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul 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