English, Article edition: MODEL RISK IN VaR ESTIMATION: AN EMPIRICAL STUDY JING YAO; ZHONG-FEI LI; KAI W. NG

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Bookmark: http://trove.nla.gov.au/version/36377
Physical Description
  • article
Language
  • English

Edition details

Title
  • MODEL RISK IN VaR ESTIMATION: AN EMPIRICAL STUDY
Author
  • JING YAO
  • ZHONG-FEI LI
  • KAI W. NG
Physical Description
  • article
Notes
  • This paper studies the model risk; the risk of selecting a model for estimating the Value-at-Risk (VaR). By considering four GARCH-type volatility processes exponentially weighted moving average (EWMA), generalized autoregressive conditional heteroskedasticity (GARCH), exponential GARCH (EGARCH), and fractionally integrated GARCH (FIGARCH), we evaluate the performance of the estimated VaRs using statistical tests including the Kupiec's likelihood ratio (LR) test, the Christoffersen's LR test, the CHI (Christoffersen, Hahn, and Inoue) specification test, and the CHI nonnested test. The empirical study based on Shanghai Stock Exchange A Share Index indicates that both EGARCH and FIGARCH models perform much better than the other two in VaR computation and that the two CHI tests are more suitable for analyzing model risk.
  • Model risk, Value-at-Risk, GARCH, statistical tests
  • RePEc:wsi:ijitdm:v:05:y:2006:i:03:p:503-512
Language
  • English
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