Regression-Based Estimation of Covariance Matrix of Stock Returns

Saejoon Kim, Soong Kim

Abstract: Covariance matrix estimation is an important problem in various fields of social science including financial economics. In this paper, we consider the estimation problem in the regression framework in order to resolve the deficiencies of the traditional methods. In particular, we establish the regression framework using support vector regression for the in-sample-based and the shrinkage-based estimation methods. Empirical results will indicate that our proposed covariance matrix estimation methods sufficiently perform superior to the two traditional estimation methods.