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The Importance of Year of Birth in Two-Dimensional Mortality Data

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  • Richards, S. J.
  • Kirkby, J. G.
  • Currie, I. D.

Abstract

Late-life mortality patterns are of crucial interest to actuaries assessing longevity risk. One important explanatory variable is year of birth. We present the results of various analyses demonstrating this, including a statistical model which lends weight to the importance of year-of-birth effects in both population and insured data. We further find that a model based on age and year of birth fits United Kingdom mortality data better than a model based on age and period, suggesting that cohort effects are more significant than period effects. The financial implications of these cohort effects are considerable for portfolios with long-term longevity exposure, such as annuities written by insurance companies and defined benefit pension schemes.

Suggested Citation

  • Richards, S. J. & Kirkby, J. G. & Currie, I. D., 2006. "The Importance of Year of Birth in Two-Dimensional Mortality Data," British Actuarial Journal, Cambridge University Press, vol. 12(1), pages 5-38, March.
    • Handle: RePEc:cup:bracjl:v:12:y:2006:i:01:p:5-38_00
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    Citations

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    Cited by:

    1. Michael Murphy, 2010. "Detecting year‐of‐birth mortality patterns with limited data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(4), pages 915-920, October.
    2. Enrique Acosta & Alyson van Raalte, 2019. "APC curvature plots: Displaying nonlinear age-period-cohort patterns on Lexis plots," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(42), pages 1205-1234.
    3. Stephen Richards, 2010. "Author's response," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(4), pages 920-924, October.
    4. Li, Johnny Siu-Hang, 2010. "Pricing longevity risk with the parametric bootstrap: A maximum entropy approach," Insurance: Mathematics and Economics, Elsevier, vol. 47(2), pages 176-186, October.
    5. 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.
    6. David Atance & Alejandro Balbás & Eliseo Navarro, 2020. "Constructing dynamic life tables with a single-factor model," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 43(2), pages 787-825, December.
    7. Kieron Barclay & Martin Kolk, 2015. "Birth Order and Mortality: A Population-Based Cohort Study," Demography, Springer;Population Association of America (PAA), vol. 52(2), pages 613-639, April.
    8. MacMinn, Richard & Weber, Frederik, 2009. "Select Birth Cohorts," Discussion Papers in Business Administration 9207, University of Munich, Munich School of Management.
    9. Redondo Lourés, Cristian & Cairns, Andrew J.G., 2021. "Cause of death specific cohort effects in U.S. mortality," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 190-199.
    10. Kung, Ko-Lun & Liu, I-Chien & Wang, Chou-Wen, 2021. "Modeling and pricing longevity derivatives using Skellam distribution," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 341-354.
    11. Basellini, Ugofilippo & Kjærgaard, Søren & Camarda, Carlo Giovanni, 2020. "An age-at-death distribution approach to forecast cohort mortality," Insurance: Mathematics and Economics, Elsevier, vol. 91(C), pages 129-143.
    12. 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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