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Forecasting VaR using realized EGARCH model with skewness and kurtosis

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  • Wu, Xinyu
  • Xia, Michelle
  • Zhang, Huanming

Abstract

This paper proposes an extension of the realized EGARCH (RealEGARCH) model, namely the RealEGARCH model with Skewness and Kurtosis (RealEGARCH-SK model) for forecasting VaR. The model is able to account for time-varying non-Gaussianities (time-varying skewness and kurtosis). The empirical analysis using the Chinese stock indices, the Shanghai Stock Exchange Composite (SSEC) and the Shenzhen Stock Exchange Component (SZSEC), demonstrates that the RealEGARCH-SK model produces more accurate extreme VaR forecasts than the standard realized GARCH (RealGARCH) and RealEGARCH models with the normal distribution and the RealEGARCH model with the skewed t-distribution (RealEGARCH-ST model).

Suggested Citation

  • Wu, Xinyu & Xia, Michelle & Zhang, Huanming, 2020. "Forecasting VaR using realized EGARCH model with skewness and kurtosis," Finance Research Letters, Elsevier, vol. 32(C).
    • Handle: RePEc:eee:finlet:v:32:y:2020:i:c:s1544612318308067
    DOI: 10.1016/j.frl.2019.01.002
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