A Comparison of Mixed Garch-Jump Models with Skewed T-Distribution for Asset Returns

This paper investigates whether alternative return distributions such as Hansen's skewed t-distribution combined with similar GARCH specifications can outperform mixed GARCH-jump models. More specifically, this study questions if complicated jumps specification such as Maheu and McCurdy (2004)'s GARJI model can provide a better fit to the empirical distribution of the data. We find that more parsimonious GJR-HT model is superior to mixed GARCH-jump models. Likelihood-ratio tests, information criteria and Value-at-Risk (VaR) analysis confirm that GJR-HT is the most suitable model specification which gives us both better fit to the data and parsimony of parameterization. The benefits of estimating GARCH models using asymmetric leptokurtic distributions are more substantial for highly volatile series such as emerging stock markets, which have a higher degree of non-normality. Furthermore, Hansen's skewed t-distribution also provides us with an excellent risk management tool evidenced by VaR analysis