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Posts feedOver-dispersion (reprise for actuaries)
In my previous post I illustrated the effects of over-dispersion in population data. Of course, an actuary could quite properly ask: why use ONS data?
Lost in translation
Actuaries have a long-standing habit of using different terminology to statisticians. This page lists some common terms used by actuaries in mortality work and their "translation" for a non-actuarial audience. The terms and notation are those used by actuaries in the UK, but in every country I have visited the local actuaries have used similar notation.
Table 1. Common actuarial terms and their definition for statisticians.
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
Simulation and survival
In an earlier post we discussed how a survival model was directly equivalent to assuming future lifetime was a random variable. One consequence of this is that survival models make it quick and simple to simulate a policyholder's future lifetime for the purposes of ICAs and Solvency II.
Run-off volatility
When investigating risk in an annuity portfolio, a key task is to simulate the future lifetime for each annuitant. Survival models make this particularly easy, as covered in an earlier posting on simulating lifetimes.
Personal standards
Love them or loathe them, actuaries cannot get by without standard tables in some shape or form. Even when performing analysis of your own experience data to avoid basis risk, standard tables are often used as a kind of lingua franca between parties, a convenient way to express approximate results in a way everyone can understand.
East meets West
This month sees the twentieth anniversary of the fall of the Berlin Wall. This is therefore an appropriate time to remind ourselves of a dramatic example of the plasticity of mortality.
Island life
We have written extensively about the use of postcodes and geodemographics for mortality modelling. Two peer-reviewed papers recently presented to the Institute of Actuaries in London have testified to the power of geodemographics when applied to pensioner mortality: Richards (2008) and Madrigal et al (2009).
Cause and effect
Examining past trends in cause of death can be very instructive. However, in some quarters it has become popular to try to extrapolate trends in causes of death to create a forecast of future mortality rates.
Fifteen-year (h)itch
Effective risk modelling is about grouping people with shared characteristics which affect this risk. In mortality analysis by far the most important risk factor is age, so it is not a good idea to mix the young and old if it can be avoided.