 Distributionally robust insurance under the Wasserstein distanceTim J. Boonen and Wenjun Jiang Insurance: Mathematics and Economics, 2025, vol. 120, issue C, 61-78 Abstract: This paper studies the optimal insurance contracting from the perspective of a decision maker (DM) who has an ambiguous understanding of the loss distribution. The ambiguity set of loss distributions is represented as a p-Wasserstein ball, with p∈Z+, centered around a specific benchmark distribution. The DM selects the indemnity function that minimizes the worst-case risk within the risk-minimization framework, considering the constraints of the Wasserstein ball. Assuming that the DM is endowed with a convex distortion risk measure and that insurance pricing follows the expected-value premium principle, we derive the explicit structures of both the indemnity function and the worst-case distribution using a novel survival-function-based representation of the Wasserstein distance. We examine a specific example where the DM employs the GlueVaR and provide numerical results to demonstrate the sensitivity of the worst-case distribution concerning the model parameters. Keywords: Optimal insurance; Robustness; Distortion risk measure; Wasserstein distance; GlueVaR (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:120:y:2025:i:c:p:61-78 DOI: 10.1016/j.insmatheco.2024.11.003 Access Statistics for this article Insurance: Mathematics and Economics is currently edited by R. Kaas, Hansjoerg Albrecher, M. J. Goovaerts and E. S. W. Shiu More articles in Insurance: Mathematics and Economics from Elsevier |
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