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MODEL RISK IN VaR ESTIMATION: AN EMPIRICAL STUDY

Author

Listed:
  • JING YAO

    (Department of Risk Management and Insurance, Lingnan (University) College, Sun Yat-Sen University, Guangzhou 510275, P. R. China)

  • ZHONG-FEI LI

    (Department of Risk Management and Insurance, Lingnan (University) College, Sun Yat-Sen University, Guangzhou 510275, P. R. China;
    Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada)

  • KAI W. NG

    (Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong, China)

Abstract

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.

Suggested Citation

  • Jing Yao & Zhong-Fei Li & Kai W. Ng, 2006. "MODEL RISK IN VaR ESTIMATION: AN EMPIRICAL STUDY," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 5(03), pages 503-512.
    • Handle: RePEc:wsi:ijitdm:v:05:y:2006:i:03:n:s021962200600209x
    DOI: 10.1142/S021962200600209X
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    Citations

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    Cited by:

    1. Abdul Hakim, 2009. "Forcasting portofolio value-at-risk for international stocks, bonds, and foreign exchange emerging market evidence," Economic Journal of Emerging Markets, Universitas Islam Indonesia, vol. 1(1), pages 13-26, April.
    2. Zhou, Wei & O'Neill, Eoghan & Moncaster, Alice & Reiner, David M. & Guthrie, Peter, 2020. "Forecasting urban residential stock turnover dynamics using system dynamics and Bayesian model averaging," Applied Energy, Elsevier, vol. 275(C).
    3. Fernanda Maria Müller & Marcelo Brutti Righi, 2018. "Numerical comparison of multivariate models to forecasting risk measures," Risk Management, Palgrave Macmillan, vol. 20(1), pages 29-50, February.

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