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

Ming-Chih Lee, Jung-Bin Su and Hung-Chun Liu

Applied Financial Economics Letters, 2008, vol. 4, issue 6, 425-431

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.

Date: 2008
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DOI: 10.1080/17446540701765274

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