Scanning the Horizon: Integrating Expert Knowledge into the Calibration of Stochastic Mortality Models

Expert knowledge from many different disciplines has the potential to inform on developments that could significantly increase or decrease human life expectancy. However, such knowledge is typically not considered in longevity risk management, since stochastic mortality models are generally only calibrated to historical mortality patterns, i.e., fully data-driven.  Following an interdisciplinary approach, we develop a methodology how expert knowledge on the (uncertainty of the) future of human life expectancy can be integrated into the calibration of stochastic mortality models. We argue that this approach is particularly relevant if there are “low probability / high impact” scenarios on the horizon, that are considered plausible by experts in their respective field but are “virtually impossible” in models calibrated to historical data. Based on current research on treatments that might be effective in slowing down ageing, we motivate and propose an exemplary plausible scenario for the future development of human life expectancy. We assign a potential impact on life expectancy as well as a plausible probability of occurrence to the scenario and present a method for calibrating stochastic mortality models so that the resulting projections are in line with these parameters. In a case study, we analyse and compare the longevity risk in an exemplary annuity portfolio and show that this so-called “driver-driven” calibration can lead to a structurally different assessment of longevity risk than the traditional “data-driven” approach, especially with regard to tail risks. Find the working paper version here.