Journal
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
Volume 5, Issue 3, Pages 503-512Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S021962200600209X
Keywords
model risk; Value-at-Risk; GARCH; statistical tests
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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 (ECARCH), 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 FICARCH models perform much better than the other two in VaR computation and that the two CHI tests are more suitable for analyzing model risk.
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