Distributionally Robust Mean-Variance Portfolio Selection with Wasserstein Distances

Jose Blanchet (), Lin Chen () and Xun Yu Zhou ()
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Jose Blanchet: Department of Management Science and Engineering, Stanford University, Stanford, California 94305
Lin Chen: Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027
Xun Yu Zhou: Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027

Management Science, 2022, vol. 68, issue 9, 6382-6410

Abstract: We revisit Markowitz’s mean-variance portfolio selection model by considering a distributionally robust version, in which the region of distributional uncertainty is around the empirical measure and the discrepancy between probability measures is dictated by the Wasserstein distance. We reduce this problem into an empirical variance minimization problem with an additional regularization term. Moreover, we extend the recently developed inference methodology to our setting in order to select the size of the distributional uncertainty as well as the associated robust target return rate in a data-driven way. Finally, we report extensive back-testing results on S&P 500 that compare the performance of our model with those of several well-known models including the Fama–French and Black–Litterman models.

Keywords: mean-variance portfolio selection; robust model; Wasserstein distance; robust Wasserstein profile inference (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:68:y:2022:i:9:p:6382-6410

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