Estimation of Time-Varying Hedge Ratios for Corn and Soybeans : Bgarch and Random Coefficient Approaches

This paper is concerned with estimation of optimal hedge ratios. Many researchers have demonstrated the inadequancies of the ordinary least squares (OLS) method that gives constant hedge ratio and suggested the use of bivariate autoregressive conditional heteroskedastic (BGARCH) model. We introduce the use of a random coefficient autoregressive (RCAR) model to estimate time varying hedge ratios. Using daily data of spot and future prices of corn and soybeans we find substantial presence of both ARCH and random coefficient effects. Hedging performance in terms of variance reduction of returns from alternative models have also been conducted