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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 - Université Claude Bernard - Lyon I : EA2429)

  • Frédéric Planchet

    ()
    (SAF - Laboratoire de Sciences Actuarielle et Financière - Université Claude Bernard - Lyon I : EA2429)

  • Pierre-Emmanuel Thérond

    ()
    (SAF - Laboratoire de Sciences Actuarielle et Financière - Université Claude Bernard - Lyon I : EA2429)

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.

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Bibliographic Info

Paper provided by HAL in its series Post-Print with number hal-00839339.

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Date of creation: 23 Jun 2013
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Publication status: Published - Presented, AFIR Colloquium, 2013, Lyon, France
Handle: RePEc:hal:journl:hal-00839339

Note: View the original document on HAL open archive server: http://hal.archives-ouvertes.fr/hal-00839339
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