Linear shrinkage estimation of large covariance matrices using factor models

JOURNAL OF MULTIVARIATE ANALYSIS

卷:152

Article

Ikeda, Yuki¹  Kubokawa, Tatsuya¹ 

[1] Univ Tokyo, Fac Econ, Bunkyo Ku, 7-3-1 Hongo, Tokyo 1130033, Japan

关键词: Covariance matrix;    Factor model;    High dimension;    Large sample;    Non-normal distribution;    Normal distribution;    Portfolio management;    Ridge-type estimator;    Risk function;   

DOI  :  10.1016/j.jmva.2016.08.001

来源: Elsevier

【 摘 要 】

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. (C) 2016 Elsevier Inc. All rights reserved.

【 授权许可】

Free