Distributionally robust insurance under the Wasserstein distance
This paper explores optimal insurance contracting for a decision maker facing ambiguous loss distributions. Using a p‑Wasserstein ball around a benchmark distribution and a convex distortion risk measure, the indemnity function and worst‑case distribution are derived. Numerical examples highlight the sensitivity of worst‑case distributions to model parameters.Distributionally robust insurance under the Wasserstein distance