The escalating frequency and severity of natural disasters, exacerbated by
climate change, underscore the critical role of insurance in facilitating
recovery and promoting investments in risk reduction. This work introduces a
novel Adaptive Robust Optimization (ARO) framework tailored for the calculation
of catastrophe insurance premiums, with a case study applied to the United
States National Flood Insurance Program (NFIP). To the best of our knowledge,
it is the first time an ARO approach has been applied to for disaster insurance
pricing. Our methodology is designed to protect against both historical and
emerging risks, the latter predicted by machine learning models, thus directly
incorporating amplified risks induced by climate change. Using the US flood
insurance data as a case study, optimization models demonstrate effectiveness
in covering losses and produce surpluses, with a smooth balance transition
through parameter fine-tuning. Among tested optimization models, results show
ARO models with conservative parameter values achieving low number of insolvent
states with the least insurance premium charged. Overall, optimization
frameworks offer versatility and generalizability, making it adaptable to a
variety of natural disaster scenarios, such as wildfires, droughts, etc. This
work not only advances the field of insurance premium modeling but also serves
as a vital tool for policymakers and stakeholders in building resilience to the
growing risks of natural catastrophes.

透过引入适用于计算灾难保险费率的新颖自适应鲁棒优化（ARO）框架并结合美国国家洪水保险计划（NFIP）的实际案例研究，本研究首次应用 ARO 方法进行灾难保险定价，并设计了保护历史和预测未来风险的方法，这样可以直接纳入气候变化所带来的加剧风险，最终提供了可行的费率设计和预测风险的保障，为保险费率建模提供了新的方法，且对政策制定者和相关利益相关者在建立对自然灾害不断增长的风险的抵御力方面提供了重要的工具。

灾难保险：一种自适应的鲁棒优化方法

Catastrophe Insurance: An Adaptive Robust Optimization Approach

Accurate time series forecasting is critical for a wide range of problems
with temporal data. Ensemble modeling is a well-established technique for
leveraging multiple predictive models to increase accuracy and robustness, as
the performance of a single predictor can be highly variable due to shifts in
the underlying data distribution. This paper proposes a new methodology for
building robust ensembles of time series forecasting models. Our approach
utilizes Adaptive Robust Optimization (ARO) to construct a linear regression
ensemble in which the models' weights can adapt over time. We demonstrate the
effectiveness of our method through a series of synthetic experiments and
real-world applications, including air pollution management, energy consumption
forecasting, and tropical cyclone intensity forecasting. Our results show that
our adaptive ensembles outperform the best ensemble member in hindsight by
16-26% in root mean square error and 14-28% in conditional value at risk and
improve over competitive ensemble techniques.

该论文提出了一种使用 Adaptive Robust Optimization 构建具有适应性权重的线性回归集成的新方法，以提高时间序列预测模型的准确性和鲁棒性，并演示了其在空气污染管理，能源消耗预测以及热带气旋强度预测等实际应用中优于其他竞争性集成技术的性能。