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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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    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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    Article provided by Taylor and Francis Journals in its journal Applied Financial Economics Letters.

    Volume (Year): 4 (2008)
    Issue (Month): 6 ()
    Pages: 425-431

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    Handle: RePEc:taf:apfelt:v:4:y:2008:i:6:p:425-431
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