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

Olivier Ledoit and Michael Wolf

Economics Working Papers from Department of Economics and Business, Universitat Pompeu Fabra

Abstract: 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 multi-factor 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 multi-factor models.

Keywords: Covariance matrix estimation; factor models; portofolio selection; shrinkage (search for similar items in EconPapers)
JEL-codes: C13 C51 C61 G11 G15 (search for similar items in EconPapers)
Date: 2001-11
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-fmk
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Improved estimation of the covariance matrix of stock returns with an application to portfolio selection (2003)
Working Paper: Improved estimation of the covariance matrix of stock returns with an application to portfolio selection (2000)
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Persistent link: https://EconPapers.repec.org/RePEc:upf:upfgen:586

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