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Mortality data reliability in an internal model

Published online by Cambridge University Press:  03 August 2020

Fabrice Balland ,
Alexandre Boumezoued Open the ORCID record for Alexandre Boumezoued [Opens in a new window] ,
Laurent Devineau ,
Marine Habart  and
Tom Popa
Fabrice Balland
Affiliation:
GIE AXA, 21 Avenue Matignon, 75008Paris, France
Alexandre Boumezoued*
Affiliation:
Milliman, 14 Avenue de la Grande Armée, 75017Paris, France
Laurent Devineau
Affiliation:
Milliman, 14 Avenue de la Grande Armée, 75017Paris, France
Marine Habart
Affiliation:
GIE AXA, 21 Avenue Matignon, 75008Paris, France
Tom Popa
Affiliation:
GIE AXA, 21 Avenue Matignon, 75008Paris, France
*
*Corresponding author. Email: alexandre.boumezoued@milliman.com
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Abstract

In this paper, we discuss the impact of some mortality data anomalies on an internal model capturing longevity risk in the Solvency 2 framework. In particular, we are concerned with abnormal cohort effects such as those for generations 1919 and 1920, for which the period tables provided by the Human Mortality Database show particularly low and high mortality rates, respectively. To provide corrected tables for the three countries of interest here (France, Italy and West Germany), we use the approach developed by Boumezoued for countries for which the method applies (France and Italy) and provide an extension of the method for West Germany as monthly fertility histories are not sufficient to cover the generations of interest. These mortality tables are crucial inputs to stochastic mortality models forecasting future scenarios, from which the extreme 0.5% longevity improvement can be extracted, allowing for the calculation of the solvency capital requirement. More precisely, to assess the impact of such anomalies in the Solvency II framework, we use a simplified internal model based on three usual stochastic models to project mortality rates in the future combined with a closure table methodology for older ages. Correcting this bias obviously improves the data quality of the mortality inputs, which is of paramount importance today, and slightly decreases the capital requirement. Overall, the longevity risk assessment remains stable, as well as the selection of the stochastic mortality model. As a collateral gain of this data quality improvement, the more regular estimated parameters allow for new insights and a refined assessment regarding longevity risk.

Keywords

MortalityLongevityCohort effectSolvency 2Internal model
Type
Paper
Information
Annals of Actuarial Science , Volume 14 , Issue 2 , September 2020 , pp. 420 - 444
Copyright
© Institute and Faculty of Actuaries 2020

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