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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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  • Hatice Gaye Gencer
  • Sercan Demiralay

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.

Suggested Citation

  • Hatice Gaye Gencer & Sercan Demiralay, 2016. "Volatility Modeling and Value-at-Risk (VaR) Forecasting of Emerging Stock Markets in the Presence of Long Memory, Asymmetry, and Skewed Heavy Tails," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 52(3), pages 639-657, March.
    • Handle: RePEc:mes:emfitr:v:52:y:2016:i:3:p:639-657
    DOI: 10.1080/1540496X.2014.998557
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    Cited by:

    1. Galip Gençyürk, 2024. "Volatility Modeling and Spillover: The Turkish and Russian Stock Markets," Istanbul Business Research, Istanbul University Business School, vol. 53(1), pages 81-101, April.
    2. Li, Longqing, 2017. "A Comparative Study of GARCH and EVT Model in Modeling Value-at-Risk," MPRA Paper 85645, University Library of Munich, Germany.
    3. Manh Ha Nguyen & Olivier Darné, 2018. "Forecasting and risk management in the Vietnam Stock Exchange," Working Papers halshs-01679456, HAL.
    4. Kaijian He & Hongqian Wang & Jiangze Du & Yingchao Zou, 2016. "Forecasting Electricity Market Risk Using Empirical Mode Decomposition (EMD)—Based Multiscale Methodology," Energies, MDPI, vol. 9(11), pages 1-11, November.
    5. Ahmed BenSaïda & Sabri Boubaker & Duc Khuong Nguyen & Skander Slim, 2018. "Value‐at‐risk under market shifts through highly flexible models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(8), pages 790-804, December.

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