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Value-at-risk in US stock indices with skewed generalized error distribution

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  • Ming-Chih Lee
  • Jung-Bin Su
  • Hung-Chun Liu

Abstract

This investigation proposes a composite Simpson's rule, a numerical integral method, for estimating quantiles on the skewed generalized error distribution (SGED). Daily spot prices of S&P500 and Dow-Jones stock indices are used as data to examine the one-day-ahead VaR (Value at Risk) forecasting performance of the GARCH-N and GARCH-SGED models. Empirical results show that the GARCH-SGED models provide more accurate VaR forecasts than the GARCH-N models for both low and high confidence levels. These findings demonstrate that the use of SGED distribution, which explicitly accommodates both skewness and kurtosis, is essential for out-of-sample VaR forecasting in US stock markets.

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  • Ming-Chih Lee & Jung-Bin Su & Hung-Chun Liu, 2008. "Value-at-risk in US stock indices with skewed generalized error distribution," Applied Financial Economics Letters, Taylor & Francis Journals, vol. 4(6), pages 425-431.
    • Handle: RePEc:taf:raflxx:v:4:y:2008:i:6:p:425-431
    DOI: 10.1080/17446540701765274
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    Cited by:

    1. Fernanda Maria Müller & Thalles Weber Gössling & Samuel Solgon Santos & Marcelo Brutti Righi, 2024. "A comparison of Range Value at Risk (RVaR) forecasting models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 509-543, April.

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