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Mortality : a statistical approach to detect model misspecification


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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hal-00839339 , version 1 (27-06-2013)


  • HAL Id : hal-00839339 , version 1


Jean-Charles Croix, Frédéric Planchet, Pierre-Emmanuel Thérond. Mortality : a statistical approach to detect model misspecification. AFIR Colloquium, Jun 2013, Lyon, France. ⟨hal-00839339⟩
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