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Dynamic VaR forecasts using conditional Pearson type IV distribution

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  • Wei Kuang

Abstract

This paper generalizes the exponentially weighted maximum likelihood (EWML) procedure to account for volatility and higher moment dynamics of the returns distribution. Prior research uses EWML to forecast value at risk (VaR) by assuming daily equity returns following a scaled t distribution. This approach does not capture the significant degree of skewness inherent in the data, which potentially leads to an underestimation of VaR. We employ the EWML procedure to estimate a time‐varying Pearson IV distribution. Our results show that VaR forecasts based on Pearson IV using the EWML procedure are generally more accurate than those generated by scaled t and generalized autoregressive conditional heteroskedasticity (GARCH)‐type models, particularly for assets with high leptokurtosis and negative skewness.

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  • Wei Kuang, 2021. "Dynamic VaR forecasts using conditional Pearson type IV distribution," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 500-511, April.
    • Handle: RePEc:wly:jforec:v:40:y:2021:i:3:p:500-511
    DOI: 10.1002/for.2726
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    3. Kuang, Wei, 2022. "The economic value of high-frequency data in equity-oil hedge," Energy, Elsevier, vol. 239(PA).

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