Return distribution predictability and its implications for portfolio selection

Zhu, Min (2013) Return distribution predictability and its implications for portfolio selection. International Review of Economics & Finance, 27, pp. 209-223.

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The inquiries to return predictability are traditionally limited to conditional mean, while literature on portfolio selection is replete with moment-based analysis with up to the fourth moment being considered. This paper develops a distribution-based framework for both return prediction and portfolio selection. More specifically, a time-varying return distribution is modeled through quantile regressions and copulas, using quantile regressions to extract information in marginal distributions and copulas to capture dependence structure. A preference function which captures higher moments is proposed for portfolio selection. An empirical application highlights the additional information provided by the distributional approach which cannot be captured by the traditional moment-based methods.

Impact and interest:

11 citations in Scopus
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8 citations in Web of Science®

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ID Code: 54037
Item Type: Journal Article
Refereed: Yes
Keywords: Return predictability, quantile regression, copula, portfolio selection
DOI: 10.1016/j.iref.2012.10.002
ISSN: 1059-0560
Divisions: Current > QUT Faculties and Divisions > QUT Business School
Current > Schools > School of Economics & Finance
Copyright Owner: Copyright 2012 Elsevier
Copyright Statement: This is the author’s version of a work that was accepted for publication in International Review of Economics & Finance. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Review of Economics & Finance, [VOL 27, (2013)] DOI: 10.1016/j.iref.2012.10.002
Deposited On: 02 Nov 2012 01:36
Last Modified: 03 Jul 2016 20:28

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