Linear shrinkage estimation of large covariance matrices using factor models

Yuki Ikeda and Tatsuya Kubokawa

Journal of Multivariate Analysis, 2016, vol. 152, issue C, 61-81

Abstract: The problem of estimating a large covariance matrix using a factor model is addressed when both the sample size and the dimension of the covariance matrix tend to infinity. We consider a general class of weighted estimators which includes (i) linear combinations of the sample covariance matrix and the model-based estimator under the factor model, and (ii) linear shrinkage estimators without factors as special cases. The optimal weights in the class are derived, and plug-in weighted estimators are proposed, given that the optimal weights depend on unknown parameters. Numerical results show that our method performs well. Finally, we provide an application to portfolio management.

Keywords: Covariance matrix; Factor model; High dimension; Large sample; Non-normal distribution; Normal distribution; Portfolio management; Ridge-type estimator; Risk function (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (6)

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DOI: 10.1016/j.jmva.2016.08.001

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