Hedging Agriculture Commodities Futures with Histogram Data Based on Conditional Copula-GJR-GARCH

Abstract

This paper aims to suggest the optimal hedge ratio for agriculture commodities using copula based GJR-GARCH models, including the conventional static and dynamic conditional copulas. High frequency data are also considered as the information for constructing the hedge ratio. To find the best fit hedging model, we use the AIC and BIC to compare the performance of the models. In order to obtain the reliable frequency data, we use the hedging effectiveness for evaluating the variance reduction of the portfolio. Our results show that dynamic Student-t, static Student-t, and static Gumbel copulas are utilized to capture the dependence structure between spot and futures of wheat. We also find that 1-h frequency provide the best information for reducing the risk of the portfolio.