English, Article edition: Value-at-risk in US stock indices with skewed generalized error distribution Ming-Chih Lee; Jung-Bin Su; Hung-Chun Liu

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Physical Description
  • article
Language
  • English

Edition details

Title
  • Value-at-risk in US stock indices with skewed generalized error distribution
Author
  • Ming-Chih Lee
  • Jung-Bin Su
  • Hung-Chun Liu
Physical Description
  • article
Notes
  • 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.
  • RePEc:taf:apfelt:v:4:y:2008:i:6:p:425-431
Language
  • English
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