A Stein-type shrinkage estimator of the covariance matrix for portfolio selections

Ruili Sun, Tiefeng Ma, Shuangzhe Liu

Research output: Contribution to journalArticle

Abstract

The covariance matrix plays a crucial role in portfolio optimization problems as the risk and correlation measure of asset returns. An improved estimation of the covariance matrix can enhance the performance of the portfolio. In this paper, based on the Cholesky decomposition of the covariance matrix, a Stein-type shrinkage strategy for portfolio weights is constructed under the mean-variance framework. Furthermore, according to the agent’s maximum expected utility value, a portfolio selection strategy is proposed. Finally, simulation experiments and an empirical study are used to test the feasibility of the proposed strategy. The numerical results show our portfolio strategy performs satisfactorily.

Original languageEnglish
Pages (from-to)931-952
Number of pages22
JournalMetrika
Volume81
Issue number8
Early online date25 May 2018
DOIs
Publication statusPublished - 1 Nov 2018

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