Article ID: | iaor201523645 |
Volume: | 33 |
Issue: | 7 |
Start Page Number: | 515 |
End Page Number: | 531 |
Publication Date: | Nov 2014 |
Journal: | Journal of Forecasting |
Authors: | Dendramis Yiannis, Spungin Giles E, Tzavalis Elias |
Keywords: | markov processes |
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.