Bivariate mixed normal GARCH models and out-of-sample hedge performances

Abstract

This study compares bivariate mixed normal GARCH models with standard bivariate GARCH models in terms of the percentage variance reduction of the out-of-sample hedged portfolio and also statistical significance tests of performance improvements using Superior Predictive Ability statistics. All competing models are applied to corn and wheat futures and empirical results demonstrate that the standard BEKK-GARCH model significantly outperforms the other competing GARCH models at shorter horizons. However, as the hedge horizon is extended to longer than 10 days, it is evident that the mixed normal BEKK-GARCH model is the best at the usual significance level of 5%.