Huang T.-H.Wang Y.-H.2019-07-242019-07-24201202776693https://scholars.lib.ntu.edu.tw/handle/123456789/414907This study compares the volatility and density prediction performance of alternative GARCH models with different conditional distribution specifications. The conditional residuals are specified as normal, skewedHyphen;t or compound Poisson (jump) distribution based upon a nonlinear 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 nonHyphen;normal models is not always significant and diminished during the sample period on those occasions when volatility experiences an obvious structural change. Copyright ? 2011 John Wiley & Sons, Ltd.density predictionGARCHjumpskewed- tvolatility forecastingjournal article10.1002/for.12172-s2.0-84863052451https://www.scopus.com/inward/record.uri?eid=2-s2.0-84863052451&doi=10.1002%2ffor.1217&partnerID=40&md5=f6dd6592011b67398bae353e55994a14