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 ...
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
multi-factor models.
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