Forecasting VaR models under Different Volatility Processes and Distributions of Return Innovations |
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| Authors: | Yiannis Dendramis Giles E. Spungin Elias Tzavalis |
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| Affiliation: | 1. Department of Economics, Athens University of Economics and Business, Athens, Greece;2. School of Economics and Finance, Queen Mary, University of London, UK |
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| Abstract: | This paper provides clear‐cut evidence that the out‐of‐sample VaR (value‐at‐risk) forecasting performance of alternative parametric volatility models, like EGARCH (exponential general autoregressive conditional heteroskedasticity) or GARCH, and Markov regime‐switching models, can be considerably improved if they are combined with skewed distributions of asset return innovations. The performance of these models is found to be similar to that of the EVT (extreme value theory) approach. The performance of the latter approach can also be improved if asset return innovations are assumed to be skewed distributed. The performance of the Markov regime‐switching model is considerably improved if this model allows for EGARCH effects, for all different volatility regimes considered. Copyright © 2014 John Wiley & Sons, Ltd. |
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| Keywords: | risk measures value at risk GARCH, EGARCH and regime‐switching models extreme value theory skewed distributions |
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