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

Author

Listed:
  • Ruili Sun

    (Southwestern University of Finance and Economics)

  • Tiefeng Ma

    (Southwestern University of Finance and Economics)

  • Shuangzhe Liu

    (University of Canberra)

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.

Suggested Citation

  • Ruili Sun & Tiefeng Ma & Shuangzhe Liu, 2018. "A Stein-type shrinkage estimator of the covariance matrix for portfolio selections," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(8), pages 931-952, November.
    • Handle: RePEc:spr:metrik:v:81:y:2018:i:8:d:10.1007_s00184-018-0663-2
    DOI: 10.1007/s00184-018-0663-2
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    References listed on IDEAS

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    Cited by:

    1. Ruili Sun & Tiefeng Ma & Shuangzhe Liu & Milind Sathye, 2019. "Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review," JRFM, MDPI, vol. 12(1), pages 1-34, March.

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