Ledoit, OlivierWolf, MichaelUniversitat Pompeu Fabra. Departament d'Economia i Empresa2017-07-262017-07-262001-11-01Journal of Empirical Finance 10, 603-621, 2003http://hdl.handle.net/10230/656This 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.application/pdfengL'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commonsinfo:eu-repo/semantics/workingPapercovariance matrix estimationfactor modelsportofolio selectionshrinkageFinance and Accountinginfo:eu-repo/semantics/openAccess