Get access

The Volatility and Density Prediction Performance of Alternative GARCH Models

Authors


Yaw-Huei Wang, College of Management, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan. E-mail: yhwang@management.ntu.edu.tw

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, 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.

Ancillary