The Empirical Minimum‐Variance Hedge
讨论了在未知真实参数（估计风险）下的决策，使用贝叶斯准则求解最小方差对冲比率，并通过模拟展示先验与样本参数差异对贝叶斯和参数确定等价对冲的影响。
Abstract Decision making under unknown true parameters (estimation risk) is discussed along with Bayes' and parameter certainty equivalent (PCE) criteria. Bayes' criterion incorporates estimation risk in a manner consistent with expected utility maximization. The PCE method, which is the most commonly used, is not consistent with expected utility maximization. Bayes' criterion is employed to solve for the minimum‐variance hedge ratio. Empirical application of Bayes' minimum‐variance hedge ratio is addressed and illustrated. Simulations show that discrepancies between prior and sample parameters may lead to substantial differences between Bayesian and PCE minimum‐variance hedges.