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Robust Wasserstein Optimization and its Application in Mean-CVaR

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  • Xin Hai
  • Kihun Nam

Abstract

We refer to recent inference methodology and formulate a framework for solving the distributionally robust optimization problem, where the true probability measure is inside a Wasserstein ball around the empirical measure and the radius of the Wasserstein ball is determined by the empirical data. We transform the robust optimization into a non-robust optimization with a penalty term and provide the selection of the Wasserstein ambiguity set's size. Moreover, we apply this framework to the robust mean-CVaR optimization problem and the numerical experiments of the US stock market show impressive results compared to other popular strategies.

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  • Xin Hai & Kihun Nam, 2023. "Robust Wasserstein Optimization and its Application in Mean-CVaR," Papers 2306.15524, arXiv.org.
    • Handle: RePEc:arx:papers:2306.15524
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    References listed on IDEAS

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