High-Dimensional Distributionally Robust Mean-Variance Efficient Portfolio Selection

Zhonghui Zhang (), Huarui Jing and Chihwa Kao
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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

Mathematics, 2023, vol. 11, issue 5, 1-16

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.

Keywords: mean variance portfolio; high dimension; distributionally robust optimization; projection robust Wasserstein distance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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