Enhancing Mean-Variance Portfolio Selection by Modeling Distributional Asymmetries

34 Pages Posted: 3 May 2013 Last revised: 21 Oct 2015

See all articles by Rand Kwong Yew Low

Rand Kwong Yew Low

University of Queensland Business School

Robert W. Faff

University of Queensland

Kjersti Aas

Norwegian Computing Center

Date Written: January 1, 2013

Abstract

Why do mean-variance (MV) models perform so poorly? In searching for an answer to this question, we estimate expected returns by sampling from a multivariate probability model that explicitly incorporates distributional asymmetries. Specifically, our empirical analysis shows that an application of copulas using marginal models that incorporate dynamic features such as autoregression, volatility clustering, and skewness to reduce estimation error in comparison to historical sampling windows. Using these copula-based models, we find that several MV-based rules exhibit statistically significant and superior performance improvements even after accounting for transaction costs. However, we find that outperforming the naive equally-weighted (1/N) strategy after accounting for transactions costs still remains an elusive task.

Keywords: elliptical copula, asymmetric marginals, mean-variance, portfolio management

JEL Classification: G11, C16

Suggested Citation

Low, Rand Kwong Yew and Faff, Robert W. and Aas, Kjersti, Enhancing Mean-Variance Portfolio Selection by Modeling Distributional Asymmetries (January 1, 2013). Available at SSRN: https://ssrn.com/abstract=2259073 or http://dx.doi.org/10.2139/ssrn.2259073

Rand Kwong Yew Low (Contact Author)

University of Queensland Business School ( email )

Building 37-411, Joyce Ackroyd
St Lucia
Brisbane, QLD 4122
Australia
+61 7 3346 8124 (Phone)
+61 7 3346 8166 (Fax)

HOME PAGE: http://www.business.uq.edu.au/staff/details/rand-low

Robert W. Faff

University of Queensland ( email )

St Lucia
Brisbane, Queensland 4072
Australia

Kjersti Aas

Norwegian Computing Center ( email )

P. O. Box 114 Blindern
0314 Oslo
Norway

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