John W. Galbraith

McGill

United States

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Forcasting Expected Shortfall with a Generalized Asymetric Student-t Distribution
Abstract
Financial returns typically display heavy tails and some skewness, and conditional variance models with these features often outperform more limited models. The difference in performance may be espe- cially 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 index and a number of individual company stocks; 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.
Galbraith, W. J., and D. Zhu, "Forcasting Expected Shortfall with a Generalized Asymetric Student-t Distribution", Centre interuniversitaire de recherche en analyse des organisations.
Authors: Galbraith
Zhu
Coders: Galbraith
Zhu
Last update
07/27/2012
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