Abstract: The risk of econometric models includes model-misspecification risk and estimation risk.Backward-looking econometric models based on frequentist statistics doesn't account for the existence of estimation risk. The Bayesian approach provides a general framework where estimation risk is naturally accounted for when considering the parameters as random variable. This article uses Bayesian approach based on MCMC simulation to estimate the optimal hedge ratio of China's copper futures market. The performance of the Bayesian hedge ratios is compared to that of alternative frequentist statistics approach.The Bayesian empirical result indicates EC-VAR model performs best and the hedging performance of VAR model significantly surpasses that of simple OLS model.On the contrary,if not accounting for estimation risk,EC-VAR model performs worst and the hedging performance of VAR model down't significantly surpass that of OLS model.

Key words: futures hedging,estimation risk, bayesian statistics, gibbs sampler frequentist statistics