用于多风险再保险的气候条件级联建模：分析与受控数值应用
Climate-Conditioned Cascade Modeling for Multi-Peril Reinsurance: Analysis and Controlled Numerical Applications
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中文总结 AI 辅助
该研究提出级联气候风险网络（CCRN）模型，用于多风险再保险的气候条件级联建模，通过分离气候条件与事件内传播，经数值实验验证其可有效分析多风险再保险的损失特征与风险驱动因素。
中文摘要 AI 辅助
气候风险通过事件顺序和依赖状态的传播相互关联，这些特征无法仅通过联合损失分布完全捕捉。本文开发了一种用于多风险再保险的级联气候风险网络（CCRN），该网络在有向无环图（DAG）上将日历尺度的气候条件与事件内传播分离。该模型结合互补对数对数触发风险与有界严重度激活，通过容量受限的需求激增变换将物理状态映射到被保险损失。对于固定冲击，事件尺度级联达到唯一有限步闭合。单调比较静态分析在矩形应力集上提供路径式上角损失界，在常见随机输入下得出透明的合约层面压力测试保证。包含Copula、贝叶斯网络基准、敏感性分析和不确定性传播的综合数值实验表明，尽管中间层价格在匹配边际依赖结构下保持稳健，但远尾和高层行为存在显著差异；方向传播、年度事件频率和依赖强度是主要风险驱动因素。本研究为所提出的架构提供了受控合成验证。
英文摘要
Climate perils are linked through event ordering and state-dependent propagation, features not fully captured by joint loss distributions alone. This paper develops a Cascading Climate Risk Network (CCRN) for multi-peril reinsurance that separates calendar-scale climate conditioning from within-event propagation on a directed acyclic graph (DAG). The model combines complementary-log-log triggering hazards with bounded severity activation, mapping physical states to insured losses via a capacity-bounded demand-surge transformation. For fixed shocks, the event-scale cascade reaches a unique finite-step closure. Monotone comparative statics provide a pathwise upper-corner loss bound over rectangular stress sets, yielding a transparent contract-level stress-testing guarantee under common aleatory inputs. Comprehensive numerical experiments, including copula and Bayesian-network benchmarks, sensitivity analyses, and uncertainty propagation, demonstrate that while central layer prices remain robust across matched-marginal dependence structures, far-tail and high-layer behaviors differ materially. Directional propagation, annual event frequency, and dependence strength emerge as the principal risk drivers. The study provides a controlled synthetic verification of the proposed architecture.
