An empirical Bayesian approach to stein-optimal covariance matrix estimation

Benjamin J. Gillen

Journal of Empirical Finance, 2014, vol. 29, issue C, 402-420

Abstract: This paper proposes a conjugate Bayesian regression model to estimate the covariance matrix of a large number of securities. Characterizing the return generating process with an unrestricted factor model, prior beliefs impose structure while preserving estimator consistency. This framework accommodates economically-motivated prior beliefs and nests shrinkage covariance matrix estimators, providing a common model for their interpretation. Minimizing posterior finite-sample square error delivers a fully-automated covariance matrix estimator with beliefs that become diffuse as the sample grows relative to the dimension of the problem. In application, this Stein-optimal posterior covariance matrix performs well in a large set of simulation experiments.

Keywords: Bayesian estimation; Covariance matrix shrinkage; Asset allocation (search for similar items in EconPapers)
JEL-codes: C11 C58 G11 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:29:y:2014:i:c:p:402-420

DOI: 10.1016/j.jempfin.2014.09.006

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