Volatility Modeling and Value-at-Risk (VaR) Forecasting of Emerging Stock Markets in the Presence of Long Memory,Asymmetry, and Skewed Heavy Tails |
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Authors: | Hatice Gaye Gencer Sercan Demiralay |
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Institution: | 1. Department of Business Administration, Yeditepe University, Atasehir, Istanbul, Turkey;2. Department of International Finance,Yeditepe University, Atasehir, Istanbul, Turkey |
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Abstract: | In this article, we elaborate some empirical stylized facts of eight emerging stock markets for estimating one-day- and one-week-ahead Value-at-Risk (VaR) in the case of both short- and long-trading positions. We model the emerging equity market returns via APARCH, FIGARCH, and FIAPARCH models under Student-t and skewed Student-t innovations. The FIAPARCH models under skewed Student-t distribution provide the best fit for all the equity market returns. Furthermore, we model the daily and one-week-ahead market risks with the conditional volatilities generated from the FIAPARCH models and document that the skewed Student-t distribution yields the best results in predicting one-day-ahead VaR forecasts for all the stock markets. The results also reveal that the prediction power of the models deteriorate for longer forecasting horizons. |
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Keywords: | emerging stock markets GARCH models Value-at-Risk long memory Kupiec test Dynamic Quantile test |
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