Nos derniers articles

This study addresses a novel risk-sharing problem where an agent maximizes expected wealth under ambiguity, penalized by a chi-squared model ambiguity. The framework generalizes monotone mean-variance preferences and accommodates multiple reference models for applications like climate risk. Explicit solutions are derived for the insurer’s optimal risk-sharing strategy, decision measure, and wealth process, which depends linearly on auxiliary processes linked to Radon-Nikodym derivatives. The model penalization parameter affects wealth variance, and the optimal strategy considers the counterparty’s model and premium. Future work could explore Lévy-Itô processes, alternative divergences, or a Stackelberg game framework.

An agent with multiple loss models optimizes risk sharing with a counterparty using a mean-variance criterion adapted for ambiguity. Under a Cramér-Lundberg loss model, the optimal risk sharing contract and wealth process are characterized. The strategy is proven admissible, and the value function verified. The optimal strategy is applied to Spanish auto insurance data with differing models from cross-validation for numerical illustrations.