Strategies with minimal norm are optimal for expected utility maximization under high model ambiguity

Laurence Carassus and Johannes Wiesel

Papers from arXiv.org

Abstract: We investigate an expected utility maximization problem under model uncertainty in a one-period financial market. We capture model uncertainty by replacing the baseline model $\mathbb{P}$ with an adverse choice from a Wasserstein ball of radius $k$ around $\mathbb{P}$ in the space of probability measures and consider the corresponding Wasserstein distributionally robust optimization problem. We show that optimal solutions converge to a strategy with minimal norm when uncertainty is increasingly large, i.e. when the radius $k$ tends to infinity.

Date: 2023-06, Revised 2024-01
New Economics Papers: this item is included in nep-mic and nep-upt
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