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

Ruili Sun (), Tiefeng Ma () and Shuangzhe Liu ()
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Ruili Sun: Southwestern University of Finance and Economics
Tiefeng Ma: Southwestern University of Finance and Economics
Shuangzhe Liu: University of Canberra

Metrika: International Journal for Theoretical and Applied Statistics, 2018, vol. 81, issue 8, No 2, 952 pages

Abstract: 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.

Keywords: Cholesky decomposition; Covariance matrix; Portfolio optimization; Stein shrinkage; Utility function (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s00184-018-0663-2

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