Value-at-risk in US stock indices with skewed generalized error distribution
AbstractThis 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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Bibliographic InfoArticle provided by Taylor and Francis Journals in its journal Applied Financial Economics Letters.
Volume (Year): 4 (2008)
Issue (Month): 6 ()
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Web page: http://www.tandfonline.com/RAFL20
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- Wu, Pei-Shan & Huang, Chien-Ming & Chiu, Chien-Liang, 2011. "Effects of structural changes on the risk characteristics of REIT returns," International Review of Economics & Finance, Elsevier, vol. 20(4), pages 645-653, October.
- Cheng-Few Lee & Jung-Bin Su, 2012. "Alternative statistical distributions for estimating value-at-risk: theory and evidence," Review of Quantitative Finance and Accounting, Springer, vol. 39(3), pages 309-331, October.
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