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

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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.