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Combining Simulation with a Biased-Randomized Heuristic to Develop Parametric Bonds for Insurance Coverage against Earthquakes

机译:将模拟与偏向随机化的启发式算法相结合,以开发参数化债券来抵御地震的保险

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The social and economic impact of natural catastrophes on communities is a concern for many governments and corporations across the globe. A class of financial instruments, parametric hedges, is emerging in the (re)insurance market as a promising approach to close the protection gap related to natural hazards. This paper focuses on the design of such parametric hedges, which have the objective of maximizing the risk transferred subject to a budget constraint. With Greece as a case study, one of the most seismic prone European regions, with limited seismic insurance penetration, this paper proposes a biased-randomized algorithm to solve the optimization problem. The algorithm hybridizes Monte Carlo simulation with a heuristic to generate a variety of solutions. A simulation stage allows for analyzing the payout distribution of each solution. Results show the impact of the problem resolution on the transferred risk and on the distribution of triggered payments.
机译:自然灾害对社区的社会和经济影响是全球许多政府和公司所关注的问题。在(再)保险市场中,作为对冲措施的一种金融工具正在出现,这是弥合与自然灾害有关的保护缺口的一种有前途的方法。本文着重于此类参数对冲的设计,其目的是在预算约束下最大程度地转移风险。以希腊为例,该国是地震多发性最强的欧洲地区之一,地震保险普及率有限,因此提出了一种偏向随机化算法来解决优化问题。该算法将蒙特卡洛模拟与启发式混合,以生成各种解决方案。仿真阶段允许分析每个解决方案的支出分布。结果显示问题解决对转移的风险和触发付款的分配的影响。

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