  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 (2016) 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 (SCR). 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.
