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A Value-at-Risk framework for longevity trend risk

Author

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  • Richards, S. J.
  • Currie, I. D.
  • Ritchie, G. P.

Abstract

Longevity risk faced by annuity portfolios and defined-benefit pension schemes is typically long-term, i.e. the risk is of an adverse trend which unfolds over a long period of time. However, there are circumstances when it is useful to know by how much expectations of future mortality rates might change over a single year. Such an approach lies at the heart of the one-year, value-at-risk view of reserves, and also for the pending Solvency II regime for insurers in the European Union. This paper describes a framework for determining how much a longevity liability might change based on new information over the course of one year. It is a general framework and can accommodate a wide choice of stochastic projection models, thus allowing the user to explore the importance of model risk. A further benefit of the framework is that it also provides a robustness test for projection models, which is useful in selecting an internal model for management purposes.

Suggested Citation

  • Richards, S. J. & Currie, I. D. & Ritchie, G. P., 2014. "A Value-at-Risk framework for longevity trend risk," British Actuarial Journal, Cambridge University Press, vol. 19(1), pages 116-139, March.
    • Handle: RePEc:cup:bracjl:v:19:y:2014:i:01:p:116-139_00
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    Citations

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    Cited by:

    1. Liu, Yanxin & Li, Johnny Siu-Hang, 2016. "It’s all in the hidden states: A longevity hedging strategy with an explicit measure of population basis risk," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 301-319.
    2. Njenga, Carolyn Ndigwako & Sherris, Michael, 2020. "Modeling mortality with a Bayesian vector autoregression," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 40-57.
    3. Börger, Matthias & Schupp, Johannes, 2018. "Modeling trend processes in parametric mortality models," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 369-380.
    4. Boonen, Tim J. & De Waegenaere, Anja & Norde, Henk, 2017. "Redistribution of longevity risk: The effect of heterogeneous mortality beliefs," Insurance: Mathematics and Economics, Elsevier, vol. 72(C), pages 175-188.
    5. Ungolo, Francesco & Kleinow, Torsten & Macdonald, Angus S., 2020. "A hierarchical model for the joint mortality analysis of pension scheme data with missing covariates," Insurance: Mathematics and Economics, Elsevier, vol. 91(C), pages 68-84.
    6. Börger, Matthias & Freimann, Arne & Ruß, Jochen, 2021. "A combined analysis of hedge effectiveness and capital efficiency in longevity hedging," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 309-326.
    7. Börger, Matthias & Russ, Jochen & Schupp, Johannes, 2021. "It takes two: Why mortality trend modeling is more than modeling one mortality trend," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 222-232.
    8. Anne G. Balter & Malene Kallestrup-Lamb & Jesper Rangvid, 2019. "The move towards riskier pensions: The importance of mortality," CREATES Research Papers 2019-22, Department of Economics and Business Economics, Aarhus University.
    9. Carlo Giovanni Camarda, 2019. "Smooth constrained mortality forecasting," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(38), pages 1091-1130.

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