Abstract
This study compares the volatility and density prediction performance of alternative GARCH models with different conditional distribution specifications. The conditional residuals are specified as normal, skewed-t or compound Poisson (jump) distribution based upon a non-linear and asymmetric GARCH (NGARCH) model framework. The empirical results for the S&P 500 and FTSE 100 index returns suggest that the jump model outperforms all other models in terms of both volatility forecasting and density prediction. Nevertheless, the superiority of the non-normal models is not always significant and diminished during the sample period on those occasions when volatility experiences an obvious structural change.
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