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

Olivier Ledoit and Michael Wolf

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

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 multifactor models.

Keywords: Covariance; matrix; estimation; Factor; models; Portfolio; selection; Shrinkage; method (search for similar items in EconPapers)
Date: 2000-11
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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 portofolio selection (2001)
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Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:10089

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