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.