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Modeling the Risk in Mortality Projections

Author

Listed:
  • Nan Zhu

    (Department of Risk Management, Smeal College of Business, Pennsylvania State University, University Park, Pennsylvania 16802)

  • Daniel Bauer

    (Department of Risk and Insurance and the Center for the Demography of Health and Aging, University of Wisconsin–Madison, Madison, Wisconsin 53706)

Abstract

This paper presents and applies models for the valuation and management of mortality-contingent exposures. Such exposures include insurance and pension benefits, as well as novel mortality-linked securities traded in financial markets. Unlike conventional approaches to modeling mortality, we consider the stochastic evolution of mortality projections rather than realized mortality rates . Relying on a time series of age-specific mortality forecasts, we develop a set of stochastic models that—unlike conventional mortality models—capture the evolution of mortality forecasts over the past 50 years. In particular, the dynamics of our models reflect the substantial observed variability of long-term projections and are therefore particularly well-suited for financial applications where long-term demographic uncertainty is relevant.

Suggested Citation

  • Nan Zhu & Daniel Bauer, 2022. "Modeling the Risk in Mortality Projections," Operations Research, INFORMS, vol. 70(4), pages 2069-2084, July.
    • Handle: RePEc:inm:oropre:v:70:y:2022:i:4:p:2069-2084
    DOI: 10.1287/opre.2021.2255
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