透明参数化模型损失巨灾债券的设计与定价：以德国风暴为例
Design and pricing of a transparent parametric-modeled loss CAT bond: application to German windstorm
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- RiskLab, Department of Mathematics, ETH Zurich（苏黎世联邦理工学院数学系风险实验室）
- Expertise Center for Climate Extremes (ECCE), Faculty of Business and Economics (HEC) - Faculty of Geosciences and Environment, University of Lausanne（洛桑大学地理与环境学院/商学院气候极端研究中心）
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中文总结 AI 辅助
本文提出一种透明参数化模型损失巨灾债券，其触发机制基于分离物理灾害、脆弱性与暴露度的成本随机场，并以德国风暴案例验证定价与基差风险，兼顾透明度与灵活性。
中文摘要 AI 辅助
巨灾债券通过从更广泛的资本市场获取承保能力，在一定程度上弥补了再保险的不足。我们提出了一种新型巨灾债券，以解决道德风险与基差风险之间已知的权衡问题。作为主要贡献，我们提出了一种完全透明且比赔偿型建模技术更易于评估的触发机制，以及对该巨灾债券进行定价的方法。这对于保险公司和公共当局而言具有现实意义，因为在气候变化背景下，自然灾害的发生频率和严重程度日益增加；同时，对于愿意进入巨灾债券市场但此前因该资产类别缺乏透明度而面临重大障碍的参与者，也具有吸引力。我们的触发机制源自一个成本随机场，该随机场将物理灾害、脆弱性函数和暴露度分离开来。这使得触发机制能够在参数型损失与建模损失之间采取灵活的形式，尤其是在考虑暴露度的情况下。我们基于影响德国的历史风暴事件进行了案例研究。利用历史风暴的风速数据，我们在再保险行业标准的空间分辨率上拟合了一个最大稳定随机场。行业损失和暴露度数据的可用性使我们能够将脆弱性组成部分校准到历史观测值。除了衡量与我们的触发机制相关的基差风险外，我们还进行了完整的模型评估并讨论了数值结果。
英文摘要
Catastrophe (cat) bonds overcome some lack of reinsurance by sourcing capacity from the wider capital markets. We present a new type of cat bond addressing the known trade-off between moral hazard and basis risk. As our main contributions we propose a trigger mechanism which is entirely transparent and simpler to evaluate compared to indemnity modeling techniques, as well as a methodology to price this cat bond. This is relevant for insurers and public authorities in a world where natural disasters are occurring with increasing frequency and severity due to climate change, but also for players willing to enter the cat bond market for whom the lack of transparency of this asset class has been a significant obstacle. Our trigger is derived from a cost random field which separates the physical hazard, a vulnerability function and the exposure. This allows the trigger to take a flexible form between parametric and modeled loss, in case exposure is taken into account. We present a case study based on historical windstorm events impacting Germany. Using wind speed data from historical storms, we fit a max-stable random field on a resolution which is standard in the reinsurance industry. The availability of industry loss and exposure data allows us to calibrate the vulnerability component to historical observations. Besides measuring the basis risk associated with our trigger, we perform a full model assessment and discuss numerical results.
