In this paper, we study an insurer's reinsurance-investment problem under a mean-variance criterion. We show that excess-loss is the unique equilibrium reinsurance strategy under a spectrally negative Lévy insurance model when the reinsurance premium is computed according to the expected value premium principle. Furthe…
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Investigates RI strategies for life insurers with LRD mortality rates.
Study optimal reinsurance and investment to minimize drawdown risk.
This paper analyzes a game between insurer and reinsurer under ambiguity and risk aversion, optimizing reinsurance and investment strategies.
Optimizes reinsurance and investment strategies to minimize ruin probability.
Optimizes investment and reinsurance strategies with unknown parameters.
In this work we investigate the optimal proportional reinsurance-investment strategy of an insurance company which wishes to maximize the expected exponential utility of its terminal wealth in a finite time horizon. Our goal is to extend the classical Cramer-Lundberg model introducing a stochastic factor which affects …
Study optimal reinsurance and investment strategies under common shocks affecting financial and actuarial markets.
In this paper the utility optimization problem for a general insurance model is studied. The reserve process of the insurance company is described by a stochastic differential equation driven by a Brownian motion and a Poisson random measure, representing the randomness from the financial market and the insurance claim…
Study of insurer games with model uncertainty in reinsurance and investment strategies.
Modeling business expansion as a stochastic control problem, the study finds that firms are incentivized to expand but may wait.
This paper investigates a hybrid stochastic differential reinsurance and investment game between one reinsurer and two insurers, including a stochastic Stackelberg differential subgame and a non-zero-sum stochastic differential subgame. The reinsurer, as the leader of the Stackelberg game, can price reinsurance premium…