Improved estimation of the covariance matrix of stock returns with an application to portofolio selection
Improved estimation of the covariance matrix of stock returns with an application to portofolio selection
Citació
- Journal of Empirical Finance 10, 603-621, 2003
Enllaç permanent
Descripció
Resum
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.Director i departament
Col·leccions
Mostra el registre complet