Title

The Empirical Minimum-Variance Hedge

Campus Units

Economics

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

1994

Journal or Book Title

American Journal of Agricultural Economics

Volume

76

Issue

1

First Page or Article ID Number

94

Last Page

104

DOI

10.2307/1243924

Abstract

Decision making under unknown true parameters (estimation risk) is discussed along with Bayes and parameter certainty equivalent (PCE) criteria. Bayes criterion provides the solution for optimal decision making under estimation risk in a manner consistent with expected utility maximization. The PCE method is not consistent with expected utility maximization, but is the approach commonly used.

Bayes criterion is applied to solve for the minimum variance hedge ratio (MVH) in two scenarios based on the multivariate normal distribution. Simulations show that discrepancies between prior and sample parameters may lead to substantial differences between Bayesian and PCE MVHs. Such discrepancies also highlight the superiority of Bayes criterion over the PCE, in the sense that the PCE method cannot not yield decision rules that contain prior (or nonsample) along with sample information.

Comments

This working paper was published as Lence, Sergio H. and Dermot J. Hayes, "The Empirical Minimum-Variance Hedge," American Journal of Agricultural Economics 76 (1994): 94–104, doi:10.2307/1243924.