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High-Dimensional Distributionally Robust Mean-Variance Efficient Portfolio Selection

Author

Listed:
  • Zhonghui Zhang

    (Institute of Banking and Money, Nanjing Audit University, Nanjing 210017, China)

  • Huarui Jing

    (Department of Economics and Finance, The University of the South, Sewanee, TN 37383, USA)

  • Chihwa Kao

    (Department of Economics, University of Connecticut, Storrs, CT 06269, USA)

Abstract

This paper introduces a novel distributionally robust mean-variance portfolio estimator based on the projection robust Wasserstein (PRW) distance. This approach addresses the issue of increasing conservatism of portfolio allocation strategies due to high-dimensional data. Our simulation results show the robustness of the PRW-based estimator in the presence of noisy data and its ability to achieve a higher Sharpe ratio than regular Wasserstein distances when dealing with a large number of assets. Our empirical study also demonstrates that the proposed portfolio estimator outperforms classic “plug-in” methods using various covariance estimators in terms of risk when evaluated out of sample.

Suggested Citation

  • Zhonghui Zhang & Huarui Jing & Chihwa Kao, 2023. "High-Dimensional Distributionally Robust Mean-Variance Efficient Portfolio Selection," Mathematics, MDPI, vol. 11(5), pages 1-16, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1272-:d:1089282
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    References listed on IDEAS

    as
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