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Mean-Variance vs. Full-Scale Optimization: Broad Evidence for the UK
Stockholm University, Faculty of Social Sciences. Stockholm University, Faculty of Social Sciences, School of Business. (Finance)ORCID iD: 0000-0003-0055-5121
Federal Reserve Bank of St Louis. (Economics Research)
Aston Business School. (Economics Department)
Lund University. (Economics Department)
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2008 (English)In: Manchester School, ISSN 1463-6786, E-ISSN 1467-9957, Vol. 76, p. 134-156Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2008. Vol. 76, p. 134-156
National Category
Business Administration
Research subject
Business Administration
Identifiers
URN: urn:nbn:se:su:diva-43257OAI: oai:DiVA.org:su-43257DiVA, id: diva2:355089
Available from: 2010-10-05 Created: 2010-10-05 Last updated: 2020-01-23Bibliographically approved

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