Selecting an innovation distribution for Garch models to improve efficiency of risk and volatility estimation

J. H.Venter and P. J. de Jongh

Journal of Risk

Abstract: ABSTRACT It has become common practice to fit Garch models to financial time series by means of pseudo-maximum likelihood. In this study we investigate the behavior of several maximum likelihood-based methods for estimating the Garch model parameters and for estimating volatility and risk measures (VAR and expected shortfall). We consider normal inverse Gaussian (NIG), skewed-T, T and non-parametric kernel densities for this purpose and compare the efficiencies of the resulting estimates with those based on the normal distribution. The NIG-based approach is found to be competitive with the other methods in most of the cases considered.

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