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

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dc.contributor.author Ledoit, Olivier
dc.contributor.author Wolf, Michael
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2011-01-25T19:32:34Z
dc.date.available 2011-01-25T19:32:34Z
dc.date.issued 2000-11
dc.identifier.uri http://hdl.handle.net/10016/10089
dc.description.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.
dc.format.mimetype application/octet-stream
dc.format.mimetype application/octet-stream
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries 00-77
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Covariance matrix estimation
dc.subject.other Factor models
dc.subject.other Portfolio selection
dc.subject.other Shrinkage method
dc.title Improved estimation of the covariance matrix of stock returns with an application to portfolio selection
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
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