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

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  • Laurence Carassus
  • Johannes Wiesel

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.

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  • Laurence Carassus & Johannes Wiesel, 2023. "Strategies with minimal norm are optimal for expected utility maximization under high model ambiguity," Papers 2306.01503, arXiv.org, revised Jan 2024.
    • Handle: RePEc:arx:papers:2306.01503
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    References listed on IDEAS

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