A0302
Title: Distributionally robust insurance under the Wasserstein distance
Authors: Wenjun Jiang - University of Calgary (Canada) [presenting]
Abstract: The optimal insurance contracting from the perspective of a decision maker (DM) who has an ambiguous understanding of the loss distribution is studied. The ambiguity set of loss distributions is represented as a pth-order Wasserstein ball, where p is an integer 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, the explicit structures of both the indemnity function and the worst-case distribution are derived using a novel survival-function-based representation of the Wasserstein distance. A specific example is examined where the DM employs the GlueVaR, and numerical results are provided to demonstrate the sensitivity of the worst-case distribution concerning the model parameters.
