IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-01149396.html
   My bibliography  Save this paper

Mortality: a statistical approach to detect model misspecification

Author

Listed:
  • Jean-Charles Croix

    (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)

  • Frédéric Planchet

    (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)

  • Pierre-Emmanuel Thérond

    (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)

Abstract

The Solvency 2 advent and the best-estimate methodology in future cash-flows valuation lead insurers to focus particularly on their assumptions. In mortality, hypothesis are critical as insurers use best-estimate laws instead of standard mortality tables. Backtesting methods, i.e. ex-post modelling validation processes , are encouraged by regulators and rise an increasing interest among practitioners and academics. In this paper, we propose a statistical approach (both parametric and non-parametric models compliant) for mortality laws backtesting under model risk. Afterwards, a specification risk is introduced assuming that the mortality law is subject to random variations. Finally, the suitability of the proposed method will be assessed within this framework.

Suggested Citation

  • Jean-Charles Croix & Frédéric Planchet & Pierre-Emmanuel Thérond, 2015. "Mortality: a statistical approach to detect model misspecification," Post-Print hal-01149396, HAL.
  • Handle: RePEc:hal:journl:hal-01149396
    Note: View the original document on HAL open archive server: https://hal.science/hal-01149396
    as

    Download full text from publisher

    File URL: https://hal.science/hal-01149396/document
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Booth, H. & Tickle, L., 2008. "Mortality Modelling and Forecasting: a Review of Methods," Annals of Actuarial Science, Cambridge University Press, vol. 3(1-2), pages 3-43, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Debonneuil, Edouard & Loisel, Stéphane & Planchet, Frédéric, 2018. "Do actuaries believe in longevity deceleration?," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 325-338.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lanza Queiroz, Bernardo & Lobo Alves Ferreira, Matheus, 2021. "The evolution of labor force participation and the expected length of retirement in Brazil," The Journal of the Economics of Ageing, Elsevier, vol. 18(C).
    2. de Jong, Piet & Tickle, Leonie & Xu, Jianhui, 2020. "A more meaningful parameterization of the Lee–Carter model," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 1-8.
    3. Salvatore Tedesco & Martina Andrulli & Markus Åkerlund Larsson & Daniel Kelly & Antti Alamäki & Suzanne Timmons & John Barton & Joan Condell & Brendan O’Flynn & Anna Nordström, 2021. "Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults," IJERPH, MDPI, vol. 18(23), pages 1-18, December.
    4. Johann Fuchs & Doris Söhnlein & Brigitte Weber & Enzo Weber, 2018. "Stochastic Forecasting of Labor Supply and Population: An Integrated Model," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 37(1), pages 33-58, February.
    5. repec:hum:wpaper:sfb649dp2009-015 is not listed on IDEAS
    6. Ahbab Mohammad Fazle Rabbi & Stefano Mazzuco, 2021. "Mortality Forecasting with the Lee–Carter Method: Adjusting for Smoothing and Lifespan Disparity," European Journal of Population, Springer;European Association for Population Studies, vol. 37(1), pages 97-120, March.
    7. Niels Haldrup & Carsten P. T. Rosenskjold, 2019. "A Parametric Factor Model of the Term Structure of Mortality," Econometrics, MDPI, vol. 7(1), pages 1-22, March.
    8. Marie-Pier Bergeron-Boucher & Vladimir Canudas-Romo & James E. Oeppen & James W. Vaupel, 2017. "Coherent forecasts of mortality with compositional data analysis," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 37(17), pages 527-566.
    9. Fuchs, Johann & Söhnlein, Doris & Weber, Brigitte & Weber, Enzo, 2017. "Forecasting labour supply and population: an integrated stochastic model," IAB-Discussion Paper 201701, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    10. Bernard Baffour & James Raymer, 2019. "Estimating multiregional survivorship probabilities for sparse data: An application to immigrant populations in Australia, 1981–2011," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 40(18), pages 463-502.
    11. Marie Angèle Cathleen Alijean & Jason Narsoo, 2018. "Evaluation of the Kou-Modified Lee-Carter Model in Mortality Forecasting: Evidence from French Male Mortality Data," Risks, MDPI, vol. 6(4), pages 1-26, October.
    12. de Jong, Piet & Tickle, Leonie & Xu, Jianhui, 2016. "Coherent modeling of male and female mortality using Lee–Carter in a complex number framework," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 130-137.
    13. Ayuso, Mercedes & Bravo, Jorge M. & Holzmann, Robert, 2021. "Getting life expectancy estimates right for pension policy: period versus cohort approach," Journal of Pension Economics and Finance, Cambridge University Press, vol. 20(2), pages 212-231, April.
    14. repec:zbw:bofism:2012_044 is not listed on IDEAS
    15. Katja Hanewald & Thomas Post & Helmut Gründl, 2011. "Stochastic Mortality, Macroeconomic Risks and Life Insurer Solvency," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 36(3), pages 458-475, July.
    16. F. Peters & J. P. Mackenbach & W. J. Nusselder, 2016. "Does the Impact of the Tobacco Epidemic Explain Structural Changes in the Decline of Mortality?," European Journal of Population, Springer;European Association for Population Studies, vol. 32(5), pages 687-702, December.
    17. Colin O’hare & Youwei Li, 2017. "Modelling mortality: are we heading in the right direction?," Applied Economics, Taylor & Francis Journals, vol. 49(2), pages 170-187, January.
    18. Kaakai, Sarah & El Karoui, Nicole, 2023. "Birth Death Swap population in random environment and aggregation with two timescales," Stochastic Processes and their Applications, Elsevier, vol. 162(C), pages 218-248.
    19. Aliki Sagianou & Peter Hatzopoulos, 2022. "Extensions on the Hatzopoulos–Sagianou Multiple-Components Stochastic Mortality Model," Risks, MDPI, vol. 10(7), pages 1-30, June.
    20. Colin O’hare & Youwei Li, 2017. "Models of mortality rates – analysing the residuals," Applied Economics, Taylor & Francis Journals, vol. 49(52), pages 5309-5323, November.
    21. Yassmin Ali & Ming Fang & Pablo A. Arrutia Sota & Stephen Taylor & Xun Wang, 2019. "Social Security Benefit Valuation, Risk, and Optimal Retirement," Risks, MDPI, vol. 7(4), pages 1-31, December.
    22. Wong, Jackie S.T. & Forster, Jonathan J. & Smith, Peter W.F., 2018. "Bayesian mortality forecasting with overdispersion," Insurance: Mathematics and Economics, Elsevier, vol. 83(C), pages 206-221.

    More about this item

    Keywords

    mortality; monitoring; detection; actuarial report; Solvency 2; model risk;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-01149396. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.