Stochastic Mortality Modeling and Longevity Risk Management in Multiple-population Context

随着人口老龄化和长寿风险转移市场的发展，可靠的方法来量化和管理长寿风险已成为政府和保险业的重要。该计划旨在为长寿风险的研究做出两个重要贡献。首先，我将构建多人口死亡率模型，该模型保留了现有参数模型的可解释性和一致性，同时包括数据驱动的组件，以实现更好的预测准确性。该程序将结合联合收割机专家判断和先进的机器学习预测技术，也适用于不太规则的数据集，如死因死亡率和小型保险组合。其次，将联合收割机保险风险管理的理论文献与现实的死亡率模型相结合，提出实用的长寿风险对冲策略。特别是，我将重点介绍使用标准化长寿证券的长寿风险管理，目的是促进在保险业使用这种标准化产品。本研究的具体目标包括：1.预测总死亡率数据对于总死亡率数据，有许多模型得到学术界和业界的广泛认可。因此，对于聚合数据，我计划使用机器学习技术（如LASSO、随机森林和神经网络）来增强这些现有模型。 2.对不太规则的死亡率数据进行预测由于数据有限，人口之间的死亡率模式不同，因此对不太规则的数据进行死亡率预测更具挑战性。对于这些数据，我计划使用数据驱动的算法来构建模型，这些模型可以捕捉不同人群的共性并产生准确的预测。3.实用长寿风险管理框架最近的文献提出了一些风险措施和保险风险管理技术，这是特别适合于评估对冲有效性使用标准化长寿证券，并确定偿付能力资本准备金在新的偿付能力监管。然而，这些研究仍处于理论阶段。我将扩展这些对冲策略，以科普真实的世界数据，并得出一个实用的长寿风险衡量和管理框架。近年来，长寿风险转移产品作为养老金计划和保险公司的长寿风险解决方案而受到欢迎。拟议的研究解决了养老金计划，保险公司和政府目前的担忧。随着加拿大人口老龄化和长寿风险转移市场的发展，政府和保险业都需要在死亡率建模和长寿风险管理方面具有高级技能的人才。我将在该计划中培训的HQP将有望满足应对加拿大老龄化社会挑战的需求。

With population aging and the development of longevity risk transfer market, reliable methods of quantifying and managing longevity risk have become important for governments and the insurance industry. This program aims to make two important contributions to the research of longevity risk. First, I will construct multi-population mortality models which preserve the interpretability and coherence of the existing parametric models while including data-driven components to achieve better predictive accuracy. The procedure will combine expert judgments and advanced machine learning predictive techniques, and is also applicable to less regular data sets, such as cause-specific mortality and small insurance portfolios. Secondly, I will combine the theoretical literature of insurance risk management with realistic mortality models, and develop practical hedging strategies of longevity risk. In particular, I will focus on longevity risk management using standardized longevity securities, with the goal of promoting the use of such standardized products in the insurance industry. Specific objectives of this research include: 1. Forecasting of aggregate mortality data For aggregate mortality data, there have been a number of models that are well recognized by both the academia and the industry. Hence, for the aggregate data, I plan to augment these existing models with machine learning techniques, such as LASSO, random forests, and neural network. 2. Forecasting of less regular mortality data Mortality forecasting for less regular data is more challenging because of limited data availability and distinct mortality patterns among populations. For these data, I plan to use data-driven algorithms to construct models that can capture the commonalities in different populations and produce accurate forecasts. 3. Practical longevity risk management framework Recent literature has proposed a number of risk measures and insurance risk management techniques, which are particularly suitable for evaluating the hedging effectiveness using standardized longevity securities, and determining solvency capital reserve under new solvency regulations. However, these studies are still at the theoretical stage. I will extend these hedging strategies to cope with real world data, and derive a practical longevity risk measure and management framework. In recent years, longevity risk transfer products have become popular as the longevity risk solution to pension plans and insurance companies. The proposed research addresses the current concerns of pension plans, insurance companies, and the government. With population aging and the development of longevity risk transfer market in Canada, both the government and insurance industry will need people with advanced skills in mortality modeling and longevity risk management. The HQPs I will train in this program will be expected to meet the needs of addressing the challenges of an aging society in Canada.