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

多人群背景下的随机死亡率建模和长寿风险管理

• 批准号: RGPIN-2020-05387
• 负责人: Li, Hong
• 金额: $ 1.68万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=740542
• 关键词: Stochastic; Mortality; Modeling; Longevity; Risk

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.

随着人口衰老和寿命风险转移市场的发展，可靠的量化和管理寿命风险的方法对政府和保险业变得重要。该计划旨在为研究寿命风险的研究做出两个重要贡献。首先，我将构建多人口的死亡率模型，以保留现有参数模型的可解释性和相干性，同时包括数据驱动的组件以实现更好的预测精度。该程序将结合专家法官和高级机器学习预测技术，也适用于较少的常规数据集，例如特定原因的死亡率和小型保险投资组合。其次，我将将保险风险管理的理论文献与现实的死亡模型相结合，并制定长寿风险的实用对冲策略。特别是，我将使用标准化的寿命证券专注于寿命风险管理，以促进在保险行业中使用此类标准化产品。这项研究的具体目标包括：1。预测总死亡率数据的总死亡率数据，学术界和行业都有许多模型认可了许多模型。因此，对于总数据，我计划使用机器学习技术（例如Lasso，Random Forests和Neural Network）来增强这些现有模型。 2。预测较少的常规死亡率数据死亡率预测较少的规则数据的挑战更大，因为数据的可用性有限和人群中的独特死亡模式。对于这些数据，我计划使用数据驱动的算法来构建可以捕获不同人群中共同点并产生准确森林的模型。 3。实际寿命风险管理框架最近提出了许多风险衡量和保险风险管理技术，这些技术特别适合使用标准化的寿命证券评估对冲有效性，并根据新的偿付能力法规确定解决资本储备。但是，这些研究仍处于理论阶段。我将扩展这些对冲策略来应对现实世界数据，并得出实用的寿命风险衡量和管理框架。近年来，随着养老金计划和保险公司的长寿风险解决方案，寿命风险转移产品已变得流行。拟议的研究涉及销售计划，保险公司和政府的当前问题。随着人口衰老和加拿大寿命风险转移市场的发展，政府和保险行业都需要具有高级死亡率建模和寿命风险管理技能的人。我将在该计划中训练的HQP将有望满足解决加拿大老龄化社会挑战的需求。