Forecasting Leading Death Causes in Australia Using Extended CreditRisk+

Abstract

Recently we developed a new framework in Hirz et al. (2015) to model stochastic mortality using extended CreditRisk methodology which is very different from traditional time series methods used for mortality modelling previously. In this framework, deaths are driven by common latent stochastic risk factors which may be interpreted as death causes like neoplasms, circulatory diseases or idiosyncratic components. These common factors introduce dependence between policyholders in the annuity portfolios or between death events in population. This framework can be used to construct life tables based on mortality rate forecast. It also provides an efficient, numerically stable algorithm for an exact calculation of the one-period loss distribution of annuities or life insurance products portfolios and associated risk measures such as value-at-risk and expected shortfall required by many regulators. Moreover this framework allows stress testing and, therefore, offers insight into how certain health scenarios influence annuity payments of an insurer. Such scenarios may include improvement in health treatments or better medication. In this paper, using publicly available data for Australia, we estimate the model using Markov chain Monte Carlo method to identify leading death causes across all age groups including long term forecast for 2031 and 2051. On top of general reduced mortality, the proportion of deaths for certain certain causes has changed massively over the period 1987 to 2011. Our model forecasts suggest that if these trends persist, then the future gives a whole new picture of mortality for people aged above 40 years. Neoplasms will become the overall number-one death cause. Moreover, deaths due to mental and behavioural disorders are very likely to surge whilst deaths due to circulatory diseases will tend to decrease. This potential increase in deaths due to mental and behavioural disorders for older ages will have a massive impact on social systems as, typically, such patients need long-term geriatric care.

Keywords: Extended CreditRisk, stochastic mortality model, life tables, annuity portfolios, life insurance portfolios, longevity risk, risk management, estimation of extended CreditRisk, Markov chain