Arbitrage-free catastrophe reinsurance valuation for compound dynamic contagion claims
In this paper, we consider catastrophe stop-loss reinsurance valuation for a reinsurance company with dynamic contagion claims. To deal with conventional and emerging catastrophic events, we propose the use of a compound dynamic contagion process for the catastrophic component of the liability. Under the premise that there is an absence of arbitrage opportunity in the market, we obtain arbitrage-free premiums for these contacts. To this end, the Esscher transform is adopted to specify an equivalent martingale probability measure. We show that reinsurers have various ways of levying the security loading on the net premiums to quantify the catastrophic liability in light of the growing challenges posed by emerging risks arising from climate change, cyberattacks, and pandemics. We numerically compare arbitrage-free catastrophe stop-loss reinsurance premiums via the Monte Carlo simulation method. Sensitivity analyzes are performed by changing the Esscher parameters and the retention level.
Code (1)
Similar Papers 제목 키워드 기반
Bayesian optimal investment and reinsurance with dependent financial and insurance risks
Major events like natural catastrophes or the COVID-19 crisis have impact both on the financial market and on claim arrival intensities and claim sizes of insurers. Thus, when optimal investment and reinsurance strategie…
Valuation of contingent convertible catastrophe bonds - the case for equity conversion
Within the context of the banking-related literature on contingent convertible bonds, we comprehensively formalise the design and features of a relatively new type of insurance-linked security, called a contingent conver…
A Hybrid Framework for Reinsurance Optimization: Integrating Generative Models and Reinforcement Learning
Reinsurance optimization is critical for insurers to manage risk exposure, ensure financial stability, and maintain solvency. Traditional approaches often struggle with dynamic claim distributions, high-dimensional const…
Computational Efficiencyreinforcement-learningReinforcement LearningReinforcement Learning (RL)Dynamic Reinsurance Treaty Bidding via Multi-Agent Reinforcement Learning
This paper develops a novel multi-agent reinforcement learning (MARL) framework for reinsurance treaty bidding, addressing long-standing inefficiencies in traditional broker-mediated placement processes. We pose the core…
Multi-agent Reinforcement Learningreinforcement-learningReinforcement LearningOptimal Robust Reinsurance with Multiple Insurers
We study a reinsurer who faces multiple sources of model uncertainty. The reinsurer offers contracts to $n$ insurers whose claims follow compound Poisson processes representing both idiosyncratic and systemic sources of …