Improved estimation of the covariance matrix of stock returns with an application to portfolio selection

This paper proposes to estimate the covariance matrix of stock returns by an optimally weighted average of two existing estimators: the sample covariance matrix and single-index covariance matrix. This method is generally known as shrinkage, and it is standard in decision theory and in empirical Bay...

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Bibliographic Details
Published inJournal of empirical finance Vol. 10; no. 5; pp. 603 - 621
Main Authors Ledoit, Olivier, Wolf, Michael
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.12.2003
Elsevier
SeriesJournal of Empirical Finance
Subjects
Covariance matrix estimation
Factor models
Portfolio selection
Shrinkage method
Shrinkage method
Portfolio selection
C13
G11
G15
Factor models
Covariance matrix estimation
C51
C61
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Summary:This paper proposes to estimate the covariance matrix of stock returns by an optimally weighted average of two existing estimators: the sample covariance matrix and single-index covariance matrix. This method is generally known as shrinkage, and it is standard in decision theory and in empirical Bayesian statistics. Our shrinkage estimator can be seen as a way to account for extra-market covariance without having to specify an arbitrary multifactor structure. For NYSE and AMEX stock returns from 1972 to 1995, it can be used to select portfolios with significantly lower out-of-sample variance than a set of existing estimators, including multifactor models.
ISSN:0927-5398
1879-1727
DOI:10.1016/S0927-5398(03)00007-0