Jose Blanchet, Lin Chen, Xun Yu Zhou (2022) Distributionally Robust Mean-Variance Portfolio Selection with Wasserstein Distances. Management Science 68(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.
Authors: Jose Blanchet, Lin Chen, Xun Yu Zhou
Publication date: 2022/9
Journal: Management Science
Volume: 68
Issue: 9
Pages: 6382-6410
Publisher: INFORMS
