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Mean-Variance Versus Full-Scale Optimization: Broad Evidence For The Uk

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Author Info
BJÖRN HAGSTRÖMER
RICHARD G. ANDERSON
JANE M. BINNER
THOMAS ELGER
BIRGER NILSSON

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Abstract

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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File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-9957.2008.01084.x
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Publisher Info
Article provided by University of Manchester in its journal Manchester School.

Volume (Year): 76 (2008)
Issue (Month): s1 (09)
Pages: 134-156
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Handle: RePEc:bla:manchs:v:76:y:2008:i:s1:p:134-156

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