General bounds on functionals of the lifetime under life table constraints in a joint actuarial-financial framework

TL;DR

This paper establishes bounds on the valuation of life insurance products that depend on lifetime and financial market factors, using observed life table data without restrictive assumptions.

Contribution

It introduces a novel framework for bounding functionals of lifetime in a joint actuarial-financial context, accommodating uncertainty in mortality rates.

Findings

Derived upper and lower bounds for lifetime functionals.

Provided two approaches: strict consistency and relaxed expectation-based.

Enhanced robustness in mortality risk management.

Abstract

In life insurance, life tables are used to estimate the survival distribution of individuals from a given population. However, these tables only provide survival probabilities at integer ages but no information about the distribution of deaths between two consecutive integer values. This incompleteness is particularly relevant for modern insurance products such as variable annuities, whose payoffs depend jointly on lifetime uncertainty and financial market performance. The valuation of such contracts must therefore be carried out in a joint actuarial-financial framework, as their values depend not only on the full information about mortality rates but also on the interaction between mortality risk, asset dynamics, and embedded guarantees. One frequent solution to this incompleteness is to postulate fractional age assumptions or mortality rate models, but it turns out that the results of the computations strongly depend on these restrictive assumptions. We hence derive upper and lower bounds of hybrid functionals of the lifetime with respect to mortality rates, which are compatible with the observed life table at integer ages and the given financial market. We derive two sets of results under distinct assumptions. In the first, we assume that each mortality trajectory is almost surely consistent with all the given one-year survival probabilities from the table. In the second, we consider a relaxed formulation that allows for deviations of the mortality rates while still being consistent in expectation with the given one-year reference survival probabilities. These distinct yet complementary approaches provide a new robust joint actuarial-financial framework for managing mortality risk in life insurance. They characterize the worst- and best-case contract values over all mortality processes that remain compatible with the observed life-table information and the financial market.