Data-driven Multiperiod Robust Mean-Variance Optimization
Mathematical Finance
2023-07-11 v2 Optimization and Control
Abstract
We study robust mean-variance optimization in multiperiod portfolio selection by allowing the true probability measure to be inside a Wasserstein ball centered at the empirical probability measure. Given the confidence level, the radius of the Wasserstein ball is determined by the empirical data. The numerical simulations of the US stock market provide a promising result compared to other popular strategies.
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Cite
@article{arxiv.2306.16681,
title = {Data-driven Multiperiod Robust Mean-Variance Optimization},
author = {Xin Hai and Gregoire Loeper and Kihun Nam},
journal= {arXiv preprint arXiv:2306.16681},
year = {2023}
}
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37 pages