Forecasting VaR models under Different Volatility Processes and Distributions of Return Innovations

Forecasting VaR models under Different Volatility Processes and Distributions of Return Innovations

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Article ID: iaor201523645
Volume: 33
Issue: 7
Start Page Number: 515
End Page Number: 531
Publication Date: Nov 2014
Journal: Journal of Forecasting
Authors: , ,
Keywords: markov processes
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.

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