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Improved estimation of the covariance matrix of stock returns with an application to portofolio selection
Ledoit, Olivier; Wolf, Michael
Universitat Pompeu Fabra. Departament d'Economia i Empresa
This paper proposes to estimate the covariance matrix of stock returnsby an optimally weighted average of two existing estimators: the samplecovariance matrix and single-index covariance matrix. This method isgenerally known as shrinkage, and it is standard in decision theory andin empirical Bayesian statistics. Our shrinkage estimator can be seenas a way to account for extra-market covariance without having to specifyan arbitrary multi-factor structure. For NYSE and AMEX stock returns from1972 to 1995, it can be used to select portfolios with significantly lowerout-of-sample variance than a set of existing estimators, includingmulti-factor models.
Finance and Accounting
covariance matrix estimation
factor models
portofolio selection
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