A State-Space Estimation of the Lee-Carter Mortality Model and Implications for Annuity Pricing
Man Chung Fung, Gareth W. Peters, Pavel V. Shevchenko

TL;DR
This paper develops a Bayesian state-space approach to the Lee-Carter mortality model, deriving posterior distributions and exploring implications for annuity pricing, highlighting the significance of longevity risk and potential mis-pricing.
Contribution
It introduces a new identification constraint and detailed Bayesian estimation for the Lee-Carter model within a state-space framework, with applications to annuity pricing.
Findings
Annuity prices can be under-valued by over 4% due to longevity risk.
Mis-pricing effects increase with age and policy maturity.
The proposed model captures the stochastic nature of mortality rates effectively.
Abstract
In this article we investigate a state-space representation of the Lee-Carter model which is a benchmark stochastic mortality model for forecasting age-specific death rates. Existing relevant literature focuses mainly on mortality forecasting or pricing of longevity derivatives, while the full implications and methods of using the state-space representation of the Lee-Carter model in pricing retirement income products is yet to be examined. The main contribution of this article is twofold. First, we provide a rigorous and detailed derivation of the posterior distributions of the parameters and the latent process of the Lee-Carter model via Gibbs sampling. Our assumption for priors is slightly more general than the current literature in this area. Moreover, we suggest a new form of identification constraint not yet utilised in the actuarial literature that proves to be a more convenient…
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Taxonomy
TopicsInsurance, Mortality, Demography, Risk Management · Global Health Care Issues · demographic modeling and climate adaptation
