We study Pareto optimality in a decentralized peer-to-peer risk-sharing market where agents’ preferences are represented by robust distortion risk measures that are not necessarily convex. We obtain a characterization of Pareto-optimal allocations of the aggregate risk in the market, and we show that the shape of the allocations depends primarily on each agent’s assessment of the tail of the aggregate risk. We quantify the latter via an index of probabilistic risk aversion, and we illustrate our results using concrete examples of popular families of distortion functions. As an application of our results, we revisit the market for flood risk insurance in the United States. We present the decentralized risk sharing arrangement as an alternative to the current centralized market structure, and we characterize the optimal allocations in a numerical study with historical flood data. We conclude with an in-depth discussion of the advantages and disadvantages of a decentralized insurance scheme in this setting.

The tonuity, proposed by Chen et al. ((2019) ASTIN Bulletin: The Journal of the IAA, 49(1), 530.), is a combination of an immediate tontine and a deferred annuity. However, its switching time from tontine to annuity is fixed at the moment the contract is closed, possibly becoming sub-optimal if mortality changes over time. This article introduces an alternative tonuity product, wherein a dynamic switching condition is pivotal, relying on the observable mortality trends within a reference population. The switching from tontine to annuity then occurs automatically once the condition is satisfied. Using data from the Human Mortality Database and UK Continuous Mortality Investigation, we demonstrate that, in a changing environment, where an unforeseen mortality or longevity shock leads to an unexpected increase or decrease in mortality rates, the proposed dynamic tonuity contract can be preferable to the regular tonuity contract.

In this paper, we explore a non-cooperative optimal reinsurance problem incorporating likelihood ratio uncertainty, aiming to minimize the worst-case risk of the total retained loss for the insurer. We establish a general relation between the optimal reinsurance strategy under the reference probability measure and the strategy in the worst-case scenario. This relation can further be generalized to insurance design problems quantified by tail risk measures. We also characterize distortion risk measures for which the insurer’s optimal strategy remains the same in the worst-case scenario. As an application, we determine the optimal policies for the worst-case scenario using an expectile risk measure. Additionally, we propose and explore a cooperative problem, which can be viewed as a general risk sharing problem between two agents in a comonotonic market. We determine the risk measure value and the optimal reinsurance strategy in the worst-case scenario for the insurer and compare the results from the non-cooperative and cooperative models.

The additive reserving model assumes the existence of volume measures such that the corresponding expected loss ratios are identical for all accident years. While classical literature assumes these volumes are known, in practice, accurate volume measures are often unavailable. The issue of uncertain volume measures in the additive model was addressed in a generalization of the loss ratio method published in 2018. The derivation is rather complex and the method is computationally intensive, especially for large loss development triangles. This paper introduces an alternative approach that leverages the well-established EM algorithm, significantly reducing computational requirements.

The growing concern over cyber risk has become a pivotal issue in the business world. Firms can mitigate this risk through two primary strategies: investing in cybersecurity practices and purchasing cyber insurance. Cybersecurity investments reduce the compromise probability, while cyber insurance transfers potential losses to insurers. This study employs a network model for the spread of infection among interconnected firms and investigates how each firm’s decisions impact each other. We analyze a non-cooperative game in which each firm aims to optimize its objective function through choices of cybersecurity level and insurance coverage ratio. We find that each firm’s cybersecurity investment and insurance purchase are strategic complements. Within this game, we derive sufficient conditions for the existence and uniqueness of Nash equilibrium and demonstrate its inefficiency. These theoretical results form the foundation for our numerical studies, allowing us compute firms’ equilibrium decisions on cybersecurity investments and insurance purchases across various network structures. The numerical results shed light on the impact of network structure on equilibrium decisions and explore how varying insurance premiums influence firms’ cybersecurity investments.

In this paper, we explore the optimal risk sharing problem in the context of peer-to-peer insurance. Using the criterion of minimizing total variance, we find that the optimal risk sharing strategy should take a linear form. Although linear risk sharing strategies have been examined in the literature, our study uncovers a significant finding: to minimize total variance, the linear strategy should be applied to the residual risks rather than the original risks, as commonly adopted in existing studies. By comparing with the existing models, we demonstrate the advantage of the linear residual risk sharing model in variance reduction and robustness. Furthermore, we develop and study a number of new models by incorporating some constraints, to reflect desirable properties required by the market. With those constraints, the optimal strategies turn out to favor market development, such as incentivize participation and guarantee fairness. A relevant model is considered at last, which establishes the connection among multiple optimization problems and provides insights on how to extend the models into a more general setup.