Longevity and statistical modelling
Date of Issue2017-02-27
College of Business (Nanyang Business School)
This dissertation consists of two studies on the modelling aspects of mortality (or longevity). In the first paper, we examine cohort extensions of the Poisson common factor model for modelling mortality of both genders jointly. Several alternatives are specified and applied to datasets from five developed regions. We find that direct parameterisation of cohort effect could improve model fitting, reduce the need for additional period factors, and produce consistent mortality forecasts for females and males. Furthermore, we find that the cohort effect appears to be gender indifferent for the populations examined and has an interaction effect with age in certain cases. The second paper explores the prediction error in mortality projection. This is important given the increasing longevity risk and the rising demand for longevity-linked products. Insofar, only parameter error and process error have been considered jointly while the issue of model error has been little studied. Here, we propose a method to account for process error, parameter error and model error in an integrated manner by modifying the semi-parametric bootstrapping technique. We apply the method to two datasets from the Continuous Mortality Investigation (CMI) and use the simulated scenarios to price the q-forward contracts with a risk-neutral approach. We find that model selection has a significant impact on the valuation results and thus it is crucial to incorporate model error in mortality projection. The third part of the dissertation surveys the current landscape of the longevity market and discusses some open issues related to the pricing of longevity products in the context of the broader literature.