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Regime-Switching Processes and Mean-Reverting Volatility Models in Value-at-Risk Estimation: Evidence from the Taiwan Stock Index

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  • Yi-Wen Chen
  • Chu-Bin Lin
  • Anthony H. Tu

Abstract

This article develops a model that can accurately forecast the volatility of Taiwan stock returns and efficiently estimate value-at-risk (VaR). Because the volatility in the Taiwan stock market has been shown to die down and shift quickly, we find that the model able to outperform others is one that allows the parameters of the volatility models to switch between regimes and conditional volatility to revert quickly to near-normal levels following extremely volatile periods. Compared with nested models, this model has the best performance in terms of the statistical fit of in-sample data and out-of-sample volatility forecasts and VaR estimates.

Suggested Citation

  • Yi-Wen Chen & Chu-Bin Lin & Anthony H. Tu, 2020. "Regime-Switching Processes and Mean-Reverting Volatility Models in Value-at-Risk Estimation: Evidence from the Taiwan Stock Index," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(12), pages 2693-2710, September.
    • Handle: RePEc:mes:emfitr:v:56:y:2020:i:12:p:2693-2710
    DOI: 10.1080/1540496X.2019.1609442
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    Cited by:

    1. Dariusz Gołȩbiewski & Tomasz Barszcz & Wioletta Skrodzka & Igor Wojnicki & Andrzej Bielecki, 2022. "A New Approach to Risk Management in the Power Industry Based on Systems Theory," Energies, MDPI, vol. 15(23), pages 1-19, November.
    2. Alkathery, Mohammed A. & Chaudhuri, Kausik & Nasir, Muhammad Ali, 2022. "Implications of clean energy, oil and emissions pricing for the GCC energy sector stock," Energy Economics, Elsevier, vol. 112(C).

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