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Mortality : a statistical approach to detect model misspecification

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  • 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, we'll introduce a specification risk supposing the mortality law true in average but subject to random variations. Finally, the suitability of our method will be assessed within this framework.

Suggested Citation

  • Jean-Charles Croix & Frédéric Planchet & Pierre-Emmanuel Thérond, 2013. "Mortality : a statistical approach to detect model misspecification," Post-Print hal-00839339, HAL.
  • Handle: RePEc:hal:journl:hal-00839339
    Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-00839339
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    References listed on IDEAS

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    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.
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    Cited by:

    1. Edouard Debonneuil & Stéphane Loisel & Frédéric Planchet, 2015. "Do actuaries believe in longevity deceleration?," Working Papers hal-01219270, HAL.

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