4 résultats pour « robustness »
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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
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The study delves into optimizing reinsurance amidst uncertainty, aiming to minimize insurer's worst-case loss. It establishes a connection between optimal strategies under a reference measure and those in worst-case scenarios, applicable to tail risk quantification. Conditions for common optimal solutions are provided, with applications to expectile risk measures explored. Cooperative and non-cooperative models are compared.
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"...we argue that... the median shortfall—that is, the median of the tail loss distribution—is a better option than the expected shortfall for setting the Basel Accords capital requirements due to statistical and economic considerations such as capturing tail risk, robustness, elicitability, backtesting, and surplus invariance."
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