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, 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.

Keywords: mortality; monitoring; detection; actuarial report; Solvency 2; model risk (search for similar items in EconPapers)
Date: 2015
New Economics Papers: this item is included in nep-age and nep-rmg
Note: View the original document on HAL open archive server: https://hal.science/hal-01149396v1
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Citations: View citations in EconPapers (2)

Published in Bulletin Français d'Actuariat, 2015, 15 (29), pp.13

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Related works:
Working Paper: Mortality: a statistical approach to detect model misspecification (2013)
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