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A Value-at-Risk framework for longevity trend risk ‐ Abstract of the London discussion

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  • Richards, Stephen

Abstract

This abstract relates to the following paper:RichardsS. J., CurrieI. D. and RitchieG. P.A Value-at-Risk framework for longevity trend risk.British Actuarial Journal, doi:10.1017/S1357321712000451

Suggested Citation

  • Richards, Stephen, 2014. "A Value-at-Risk framework for longevity trend risk ‐ Abstract of the London discussion," British Actuarial Journal, Cambridge University Press, vol. 19(1), pages 157-167, March.
    • Handle: RePEc:cup:bracjl:v:19:y:2014:i:01:p:157-167_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. 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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