Improved estimation of the covariance matrix of stock returns with an application to portfolio selection
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 Bay...
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Published in | Journal of empirical finance Vol. 10; no. 5; pp. 603 - 621 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
01.12.2003
Elsevier |
Series | Journal of Empirical Finance |
Subjects | |
Online Access | Get full text |
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Summary: | 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 multifactor 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. |
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ISSN: | 0927-5398 1879-1727 |
DOI: | 10.1016/S0927-5398(03)00007-0 |