Bayesian Shrinkage of Portfolio Weights

40 Pages Posted: 10 Feb 2016 Last revised: 27 Jun 2016

See all articles by Christoph Frey

Christoph Frey

Joh. Berenberg, Gossler & Co. KG; Lancaster University - Lancaster University Management School

Winfried Pohlmeier

University of Konstanz - Department of Economics & Center of Finance & Econometrics (CoFE)

Date Written: June 27, 2016

Abstract

We propose a novel regression approach for optimizing portfolios by means of Bayesian regularization techniques. In particular, we represent the weight deviations of the global minimum variance portfolio from a reference portfolio (e.g. the naive 1/N portfolio) as coefficients of a linear regression and shrink them towards zero through Bayesian shrinkage priors. By doing so, we aim to robustify the portfolios against estimation risk. Modeling the optimal portfolio weights through Bayesian priors avoids estimating the moments of the asset return distribution and substantially reduces the dimensionality of the estimation problem. We compare the proposed Bayesian shrinkage strategies to popular frequentist approaches and find that the former show better out-of-sample performance based on various performance criteria. They also turn out to be particularly attractive for high-dimensional portfolios.

Keywords: Bayesian regression, estimation risk, global minimum variance portfolio, shrinkage

JEL Classification: C11, C58, G11

Suggested Citation

Frey, Christoph and Pohlmeier, Winfried, Bayesian Shrinkage of Portfolio Weights (June 27, 2016). Available at SSRN: https://ssrn.com/abstract=2730475 or http://dx.doi.org/10.2139/ssrn.2730475

Christoph Frey (Contact Author)

Joh. Berenberg, Gossler & Co. KG ( email )

Neuer Jungfernstieg 20
Hamburg, 20354
Germany

Lancaster University - Lancaster University Management School ( email )

Bailrigg
Lancaster, LA1 4YX
United Kingdom

Winfried Pohlmeier

University of Konstanz - Department of Economics & Center of Finance & Econometrics (CoFE) ( email )

Konstanz, D-78457
Germany

HOME PAGE: http://econometrics.wiwi.uni-konstanz.de

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