Iain Currie
Dr Iain Currie was an Associate Professor in the School of Mathematical and Computer Sciences at Heriot-Watt University, and a long-term collaborator of the Longevitas team. He sadly passed on 24th May 2022 and is greatly missed.
Articles written by Iain Currie
Everything counts in large amounts
Models for projecting mortality are typically built using information on lives with deaths by age and gender. However, this ignores an important risk factor for longevity, namely socio-economic group. For annuity and pension reserving, therefore, it would be helpful to use such information when building stochastic projection models.
Forecasting mortality at high ages
The forecasting of future mortality at high ages presents additional challenges to the actuary. As an illustration of the problem, let us consider the CMI assured-lives data set for years 1950–2005 and ages 40–100 (see Stephen's blog posts on selection and data volumes). The blue curve (partly hidden under the green curve) in Figure 1 shows observed log(mortality) averaged over time.
Volatility v. Trend Risk
Getting the rough with the smooth
Over-dispersion (reprise for actuaries)
Over-dispersion
Actuaries need to project mortality rates into the far future for calculating present values of pension and annuity liabilities. In an earlier post Stephen wrote about the advantages of stochastic projection methods. One method we might try is the two-dimensional P-spline method with the simple assumption that the number of deaths at age i in year j follows a Poisson distribution (Brouhns, et al, 2002). Figure 1 shows observed and fitted log mortalities for the cross-section of the
Forecasting with limited portfolio data
The Lee-Carter Family
In a recent paper presented to the Faculty of Actuaries, Stephen Richards and I discussed model risk and showed how it can have a material impact on mortality forecasts. Different models have different features, some more desirable than others. This post illustrates a particular problem with the original Lee-Carter model, and shows how it can be combatted via smoothing. The choice of which parameters to smooth in the Lee-Carter model leads to a general family
Basis risk in mortality projections
In a recent paper Stephen Richards and I discuss the effect of model choice on mortality forecasts. Our approach is quite low key: we look at just three models, all members of the Lee-Carter family. Nevertheless, our findings are quite dramatic: even within this very small family the differences in the forecasts really matter financially. So model choice matters.