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Age-Specific Adjustment Of Graduated Mortality

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  • Salhi, Yahia
  • Thérond, Pierre-E.

Abstract

Recently, there has been an increasing interest from life insurers to assess their portfolios' mortality risks. The new European prudential regulation, namely Solvency II, emphasized the need to use mortality and life tables that best capture and reflect the experienced mortality, and thus policyholders' actual risk profiles, in order to adequately quantify the underlying risk. Therefore, building a mortality table based on the experience of the portfolio is highly recommended and, for this purpose, various approaches have been introduced into actuarial literature. Although such approaches succeed in capturing the main features, it remains difficult to assess the mortality when the underlying portfolio lacks sufficient exposure. In this paper, we propose graduating the mortality curve using an adaptive procedure based on the local likelihood. The latter has the ability to model the mortality patterns even in presence of complex structures and avoids relying on expert opinions. However, such a technique fails to offer a consistent yet regular structure for portfolios with limited deaths. Although the technique borrows the information from the adjacent ages, it is sometimes not sufficient to produce a robust life table. In the presence of such a bias, we propose adjusting the corresponding curve, at the age level, based on a credibility approach. This consists in reviewing the assumption of the mortality curve as new observations arrive. We derive the updating procedure and investigate its benefits of using the latter instead of a sole graduation based on real datasets. Moreover, we look at the divergences in the mortality forecasts generated by the classic credibility approaches including Hardy–Panjer, the Poisson–Gamma model and the Makeham framework on portfolios originating from various French insurance companies.

Suggested Citation

  • Salhi, Yahia & Thérond, Pierre-E., 2018. "Age-Specific Adjustment Of Graduated Mortality," ASTIN Bulletin, Cambridge University Press, vol. 48(2), pages 543-569, May.
  • Handle: RePEc:cup:astinb:v:48:y:2018:i:02:p:543-569_00
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    References listed on IDEAS

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    1. Tomas, Julien & Planchet, Frédéric, 2013. "Multidimensional smoothing by adaptive local kernel-weighted log-likelihood: Application to long-term care insurance," Insurance: Mathematics and Economics, Elsevier, vol. 52(3), pages 573-589.
    2. Yahia Salhi & Pierre-Emmanuel Thérond & Julien Tomas, 2016. "A Credibility Approach of the Makeham Mortality Law," Post-Print hal-01232683, HAL.
    3. Nielsen, Jens Perch & Sandqvist, Bjørn Lunding, 2000. "Credibility Weighted Hazard Estimation," ASTIN Bulletin, Cambridge University Press, vol. 30(2), pages 405-417, November.
    4. Hardy, M.R. & Panjer, H.H., 1998. "A Credibility Approach to Mortality Risk," ASTIN Bulletin, Cambridge University Press, vol. 28(2), pages 269-283, November.
    5. James Vaupel & Kenneth Manton & Eric Stallard, 1979. "The impact of heterogeneity in individual frailty on the dynamics of mortality," Demography, Springer;Population Association of America (PAA), vol. 16(3), pages 439-454, August.
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    Cited by:

    1. Blake, David & Cairns, Andrew J.G., 2021. "Longevity risk and capital markets: The 2019-20 update," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 395-439.
    2. Apostolos Bozikas & Georgios Pitselis, 2019. "Credible Regression Approaches to Forecast Mortality for Populations with Limited Data," Risks, MDPI, vol. 7(1), pages 1-22, February.
    3. Ying Jiao & Yahia Salhi & Shihua Wang, 2021. "Dynamic Bivariate Mortality Modelling," Working Papers hal-03244324, HAL.
    4. Ying Jiao & Yahia Salhi & Shihua Wang, 2022. "Dynamic Bivariate Mortality Modelling," Methodology and Computing in Applied Probability, Springer, vol. 24(2), pages 917-938, June.

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