Mortality: a statistical approach to detect model misspecification

Jean-Charles Croix (), Frédéric Planchet () and Pierre-Emmanuel Thérond
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Jean-Charles Croix: LSAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon

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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.

Date: 2013-06-23
New Economics Papers: this item is included in nep-dem and nep-ecm
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Published in AFIR Colloquium, Jun 2013, Lyon, France

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Related works:
Working Paper: Mortality: a statistical approach to detect model misspecification (2015)
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