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

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Listed:
  • 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.

Suggested Citation

  • 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 and Francis Journals, vol. 4(6), pages 425-431.
  • Handle: RePEc:taf:apfelt:v:4:y:2008:i:6:p:425-431
    DOI: 10.1080/17446540701765274
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    Citations

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    Cited by:

    1. Daniele Coin, 2017. "A goodness-of-fit test for Generalized Error Distribution," Temi di discussione (Economic working papers) 1096, Bank of Italy, Economic Research and International Relations Area.
    2. 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.
    3. repec:rjr:romjef:v::y:2017:i:4:p:97-115 is not listed on IDEAS
    4. Stavros Stavroyiannis, 2017. "A note on the Nelson Cao inequality constraints in the GJR-GARCH model: Is there a leverage effect?," Papers 1705.00535, arXiv.org.
    5. 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.

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