Regression-based estimation of covariance matrix of stock returns

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초록

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. © 2018 The authors and IOS Press. All rights reserved.

키워드

Covariance matrix estimationIn-sample-based estimationShrinkage-based estimationSupport vector regressionTime series regression
제목
Regression-based estimation of covariance matrix of stock returns
저자
Kim, SaejoonKim, Soong
DOI
10.3233/978-1-61499-927-0-182
발행일
2018
유형
Conference Paper
저널명
Frontiers in Artificial Intelligence and Applications
309
페이지
182 ~ 188