ABSTRACTMortality improvement has traditionally been analysed using an array of statistical methods, and extrapolated to make actuarial projections. This paper presents a forward-looking approach to longevity risk analysis which is based on stochastic modelling of the underlying causes of mortality improvement, due to changes in lifestyle, health environment, and advances in medical science. The rationale for this approach is similar to that adopted for modelling other types of dynamic insurance risk, e.g. natural catastrophes, where risk analysts construct a stochastic ensemble of events that might happen in the future, rather than rely on a retrospective analysis of the non-stationary and comparatively brief historical record.Another feature of prospective longevity risk analysis, which is shared with catastrophe risk modelling, is the objective of capturing vulnerability data at a high resolution, to maximise the benefit of detailed modelling capability down to individual risk factor level. Already, the use by insurers of postcode data for U.K. flood risk assessment has carried over to U.K. mortality assessment. Powered by fast numerical computation and parameterised with high quality geographical data, hydrological models of flood risk have superseded the traditional statistical insurance loss models. A decade later, medically-motivated computational models of mortality risk can be expected to gain increasing prominence in longevity risk management.
Firm‐Driven Management of Longevity Risk: Analysis of Lump‐Sum Forward Payments in Japanese Nursing Homes