Mean-Variance Versus Full-Scale Optimization: Broad Evidence for the UK

Manchester School (Impact Factor: 0.26). 09/2008; 76(s1):134-156. DOI: 10.1111/j.1467-9957.2008.01084.x
Source: RePEc


Portfolio choice by full-scale optimization applies the empirical return distribution to a parameterized utility function, and the maximum is found through numerical optimization. Using a portfolio choice setting of three UK equity indices we identify several utility functions featuring loss aversion and prospect theory, under which full-scale optimization is a substantially better approach than the mean-variance approach. As the equity indices have return distributions with small deviations from normality, the findings indicate much broader usefulness of full-scale optimization than has earlier been shown. The results hold in- and out-of-sample, and the performance improvements are given in terms of utility as well as certainty equivalents. Copyright © 2008 The Authors. Journal compilation © 2008 Blackwell Publishing Ltd and The University of Manchester.

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Available from: Jane M Binner, Apr 07, 2014
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