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From regulatory life tables to stochastic mortality projections: The exponential decline model

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  • Denuit, Michel
  • Trufin, Julien

Abstract

Often in actuarial practice, mortality projections are obtained by letting age-specific death rates decline exponentially at their own rate. Many life tables used for annuity pricing are built in this way. The present paper adopts this point of view and proposes a simple and powerful mortality projection model in line with this elementary approach, based on the recently studied mortality improvement rates. Two main applications are considered. First, as most reference life tables produced by regulators are deterministic by nature, they can be made stochastic by superposing random departures from the assumed age-specific trend, with a volatility calibrated on market or portfolio data. This allows the actuary to account for the systematic longevity risk in solvency calculations. Second, the model can be fitted to historical data and used to produce longevity forecasts. A number of conservative and tractable approximations are derived to provide the actuary with reasonably accurate approximations for various relevant quantities, available at limited computational cost. Besides applications to stochastic mortality projection models, we also derive useful properties involving supermodular, directionally convex and stop-loss orders.

Suggested Citation

  • Denuit, Michel & Trufin, Julien, 2016. "From regulatory life tables to stochastic mortality projections: The exponential decline model," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 295-303.
  • Handle: RePEc:eee:insuma:v:71:y:2016:i:c:p:295-303
    DOI: 10.1016/j.insmatheco.2016.09.015
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    References listed on IDEAS

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    1. Gbari, Kock Yed Ake Samuel & Denuit, Michel, 2016. "Stochastic approximations in CBD mortality projection models," LIDAM Reprints ISBA 2016003, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Gbari, Samuel & Denuit, Michel, 2014. "Efficient approximations for numbers of survivors in the Lee–Carter model," Insurance: Mathematics and Economics, Elsevier, vol. 59(C), pages 71-77.
    3. Denuit, M. & Haberman, S. & Renshaw, A.E., 2010. "Comonotonic Approximations to Quantiles of Life Annuity Conditional Expected Present Values: Extensions to General Arima Models and Comparison with the Bootstrap," ASTIN Bulletin, Cambridge University Press, vol. 40(1), pages 331-349, May.
    4. Müller, Alfred & Scarsini, Marco, 2000. "Some Remarks on the Supermodular Order," Journal of Multivariate Analysis, Elsevier, vol. 73(1), pages 107-119, April.
    5. Haberman, Steven & Renshaw, Arthur, 2013. "Modelling and projecting mortality improvement rates using a cohort perspective," Insurance: Mathematics and Economics, Elsevier, vol. 53(1), pages 150-168.
    6. Denuit, Michel & Haberman, S. & Renshaw, A.E., 2010. "Comonotonic Approximations To Quantiles of Life Annuity Conditional Expected Present Values: Extensions To General ARIMA Models and Comparison With the Bootstrap," LIDAM Discussion Papers ISBA 2010011, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    7. Haberman, Steven & Renshaw, Arthur, 2012. "Parametric mortality improvement rate modelling and projecting," Insurance: Mathematics and Economics, Elsevier, vol. 50(3), pages 309-333.
    8. Cadena, Meitner & Denuit, Michel, 2015. "Semi-parametric accelerated hazard Relational models with applications to Mortality projections," LIDAM Discussion Papers ISBA 2015013, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    9. Denuit, Michel & Haberman, S. & Renshaw, A. E., 2010. "Comonotonic Approximations To Quantiles of Life Annuity Conditional Expected Present Values: Extensions To General Arima Models and Comparison With the Bootstrap," LIDAM Reprints ISBA 2010028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    10. Mitchell, Daniel & Brockett, Patrick & Mendoza-Arriaga, Rafael & Muthuraman, Kumar, 2013. "Modeling and forecasting mortality rates," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 275-285.
    11. Schinzinger, Edo & Denuit, Michel & Christiansen, Marcus, 2014. "An Evolutionary Credibility Model of Lee-Carter Type for Mortality Improvement Rates," LIDAM Discussion Papers ISBA 2014021, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    12. Denuit, Michel & Haberman, Steven & Renshaw, Arthur E., 2013. "Approximations for quantiles of life expectancy and annuity values using the parametric improvement rate approach to modelling and projecting mortality," LIDAM Reprints ISBA 2013026, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    13. Brouhns, Natacha & Denuit, Michel & Vermunt, Jeroen K., 2002. "A Poisson log-bilinear regression approach to the construction of projected lifetables," Insurance: Mathematics and Economics, Elsevier, vol. 31(3), pages 373-393, December.
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    1. Albrecher, Hansjörg & Bladt, Martin & Bladt, Mogens & Yslas, Jorge, 2022. "Mortality modeling and regression with matrix distributions," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 68-87.

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