Forecasting Leading Death Causes in Australia using Extended CreditRisk$+$
Pavel V. Shevchenko, Jonas Hirz, Uwe Schmock

TL;DR
This paper introduces a novel stochastic mortality modeling framework using extended CreditRisk$^+$, applied to Australian data, to forecast leading death causes and assess impacts of health trends on future mortality and social systems.
Contribution
It applies an innovative CreditRisk$^+$ based model to mortality data, enabling detailed cause-specific forecasts and stress testing for future health scenarios.
Findings
Neoplasms will become the leading cause of death after 2011.
Deaths due to mental and behavioral disorders are projected to surge.
Circulatory disease deaths are expected to decrease.
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 annuity portfolios or between death events in population. This framework can be used to construct life tables based on mortality rate forecast. 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…
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Taxonomy
TopicsInsurance, Mortality, Demography, Risk Management · Global Health Care Issues · Health disparities and outcomes
