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Forecasting Expected Shortfall With A Generalized Asymmetric Student-T Distribution

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  • John Galbraith

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  • Dongming Zhu

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Abstract

Financial returns typically display heavy tails and some skewness, and cinditional vairance models with these features often outperform more limited models. The difference in performance may be especially important in estimating quantities that depend on tail features, including risk measures such as the expected shortfall. Here, using a recent generalization of the asymmetric Student-t distribution to allow separate parameters to control skewness and the thickness of each tail, we fit daily financial returns and forecast expected shortfall for the S&P 500 composite index; the generalized distribution is used for the standardized innovations in a nonlinear, asymmetric GARCH-type model. The results provide empirical evidence for the usefulness of the generalized distribution in improving prediction of downside market risk of financial assets.

Suggested Citation

  • John Galbraith & Dongming Zhu, 2009. "Forecasting Expected Shortfall With A Generalized Asymmetric Student-T Distribution," Departmental Working Papers 2009-01, McGill University, Department of Economics.
  • Handle: RePEc:mcl:mclwop:2009-01
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    Cited by:

    1. Kumiega, Andrew & Neururer, Thaddeus & Van Vliet, Ben, 2011. "Independent component analysis for realized volatility: Analysis of the stock market crash of 2008," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(3), pages 292-302, June.
    2. Chen, Qian & Gerlach, Richard & Lu, Zudi, 2012. "Bayesian Value-at-Risk and expected shortfall forecasting via the asymmetric Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3498-3516.
    3. Zhu, Dongming & Galbraith, John W., 2010. "A generalized asymmetric Student-t distribution with application to financial econometrics," Journal of Econometrics, Elsevier, vol. 157(2), pages 297-305, August.
    4. Richard Gerlach & Zudi Lu & Hai Huang, 2013. "Exponentially Smoothing the Skewed Laplace Distribution for Value‐at‐Risk Forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(6), pages 534-550, September.

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    JEL classification:

    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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