Temporal and spatial correlations in mortality dynamics: Applications to mortality/longevity risk management

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The stochastic nature of mortality dynamics has long been recognized. It affects the financial industry in two dimensions. On one hand, providers of pensions and annuities are subject to longevity risk, as an unexpected increase in life expectancy would lengthen the duration over which pension/annuity payments are made. According to the International Monetary Fund, global longevity risk exposure estimates range from 15 to 25 trillion USD. On the other hand, life insurers are exposed to risks arising from catastrophic mortality events, which would lead to excess mortality and consequently higher life insurance payouts. Research has found that a moderate influenza pandemic similar to the 1957 and 1968 outbreaks could cost US life insurers 15 billion USD in additional claims. The impact of the COVID-19 pandemic on life insurance payouts is yet to be estimated, but it is likely to be very significant given its scale and global presence. The long-term goal of the proposed research is to develop a better understanding of mortality and longevity risks, with a focus on the interdependency among the mortality dynamics of large collections of populations, and the financial implications of such an interdependency. This research stands out from the abundant existing works on this broad theme in its focus on the global connections among mortality/longevity exposures. It is also unique in its attention to recent mortality trends and events, including the decline in life expectancy observed in some developed countries since the mid-2010s. Specifically, the objectives of the proposed research are threefold: The first objective is to study spatial correlations of mortality improvement rates with advanced high-dimensional data analytic techniques. By exploiting spatial correlations, I will identify diversification opportunities and develop hedging strategies that are likely to outperform existing ones. The second objective is to develop a model that captures catastrophic mortality events in multiple regions simultaneously. The model will not only aid pricing reinsurance and mortality catastrophe bonds, but also improve risk management solutions for insurers with multi-country mortality risk exposures. The third objective is to develop a method to capture waves of mortality improvement using advanced temporal correlation models. Using the resulting model, I will develop regime-dependent hedging strategies, which would allow more efficient mitigation of longevity risk. The market for longevity risk transfers is growing rapidly in Canada. In 2019 alone, four multi-billion dollar deals were closed by Canadian reinsurers. Canada's financial industry now needs the knowledge on advanced stochastic mortality modelling and risk management methods, which students will acquire in the proposed research. Canada's position in the market will also be strengthened by the research outcomes, as they are being adopted by practitioners in pension and reinsurance sectors.