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

死亡率动态的随机性早已被认识到。它对金融业有两个方面的影响。一方面，养老金和年金的提供者面临长寿风险，因为预期寿命的意外增加将延长养老金/年金的支付期限。根据国际货币基金组织的数据，全球长寿风险敞口估计在15万亿至25万亿美元之间。另一方面，人寿保险公司面临由灾难性死亡事件引起的风险，这将导致超额死亡，从而导致更高的人寿保险赔付。研究发现，类似于1957年和1968年爆发的中度流感大流行可能会让美国人寿保险公司额外损失150亿美元。新冠肺炎疫情对寿险赔付的影响尚无法估计，但考虑到其规模和全球影响力，影响可能非常重大。拟议研究的长期目标是更好地了解死亡率和长寿风险，重点是大量人口的死亡率动态之间的相互依存关系，以及这种相互依存关系所涉经费问题。这项研究从关于这一广泛主题的大量现有著作中脱颖而出，其重点是死亡率/寿命暴露之间的全球联系。它还独特地注意到最近的死亡率趋势和事件，包括自2010年代中期以来在一些发达国家观察到的预期寿命下降。具体地说，拟议研究的目标有三个：第一个目标是利用先进的高维数据分析技术研究死亡率改善率的空间相关性。通过利用空间相关性，我将发现分散投资的机会，并开发可能超过现有对冲策略的对冲策略。第二个目标是开发一种模型，同时捕捉多个地区的灾难性死亡事件。该模型不仅将有助于再保险和死亡巨灾债券的定价，还将改善拥有多个国家死亡风险敞口的保险公司的风险管理解决方案。第三个目标是开发一种方法，利用先进的时间相关模型来捕捉死亡率改善的趋势。利用得到的模型，我将开发依赖于制度的对冲策略，这将使长寿风险得到更有效的缓解。在加拿大，长寿风险转移市场正在迅速增长。仅在2019年，加拿大再保险公司就完成了四笔价值数十亿美元的交易。加拿大的金融业现在需要先进的随机死亡率建模和风险管理方法方面的知识，学生将在拟议的研究中获得这些知识。研究结果也将加强加拿大在市场上的地位，因为它们正被养老金和再保险部门的从业者采用。

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.