Volatility modeling and value-at-risk (VaR) forecasting of emerging stock markets in the presence of long memory, asymmetry, and skewed heavy tails

• Autores: Hatice Gaye Gencer, Sercan Demiralay
• Localización: Emerging Markets Finance & Trade, ISSN-e 1558-0938, Vol. 52, Nº. 3, 2016, págs. 639-657
• Idioma: inglés
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• Resumen

  • 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