Speaker(s): Janinke Tol, Bianca Meijer (KPMG)
Embedding time varying experience factors in projection mortality tables
Further information is presented in a detailed paper: Tol_Paper.pdf
This work provides an extension of the stochastic Li-Lee model used in the Netherlands to determine projection mortality tables. In short the rationale of the model is that the future mortality rates for a country are estimated by using the trend in mortality rates of a larger group of countries, the peer group. For the Netherlands, the peer group contains the deaths and exposures of all European countries with a GDP above the European average. Subsequently, an extra set of parameters is computed to measure the difference between the trend in mortality rates of the peer group and that of the country of interest. This model results in a stable mortality projection that can be applied all over Europe depending on the choice for the peer group. Especially for countries with a smaller population and lesser data the model could help to get more stable mortality projections, but also for countries with bigger populations the use of a broader dataset might be beneficial.
In this presentation an extension of the model with a third dataset is investigated. The objective of this extension is to not only include European and country specific data, but to be able to create projection mortality tables calibrated on portfolio specific data of an insurer or pension fund. Using this extended model results in a projection mortality table that incorporates time varying experience rates, therefore application of separate experience factors to the projection table to estimate the population mortality are not required anymore. Besides using portfolio specific mortality data, the possibility to create mortality tables differentiated on, for example, income or educational level is also investigated. This enables insurers and pension funds with limited available data, for example for small portfolios, to create time varying experience mortality tables as well.