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Self-selection

Actuaries valuing pension liabilities need to make projections of future mortality rates.  The future is inherently uncertain, so it is best to use stochastic models of mortality.  Unfortunately, such models require a long enough time series, but few (if any) portfolios have such data.  In the UK actuaries typically rely on one of two alternative data sets: the England & Wales data from the ONS, which goes back to 1961, or the "assured lives" data from the CMI, wh

Written by: Stephen RichardsTags: basis risk, CMI

Measuring obesity

Obesity is a public-health concern throughout the developed world, since it is linked to a variety of chronic conditions such as diabetes.
Written by: Stephen RichardsTags: obesity, BMI, concentration risk, basis risk

Are annuities expensive enough?

The rationale of any business is to make a profit. This is usually achieved by selling things for more than they cost to make or supply.
Written by: Stephen RichardsTags: annuities, profit

Residual concerns

One of the most important means of checking a model's fit is to look at the residuals, i.e. the standardised differences between the actual data observed and what the model predicts.  One common definition, known as the Pearson residual, is as follows:

\[r = \frac{D-E}{\sqrt{E}}\qquad(1)\]

Written by: Stephen RichardsTags: residual, deviance residuals, Pearson residuals

Beginner's guide to postcode pricing

We've created a short graphical summary of the application of postcode-driven lifestyle within actuarial mortality models.
Written by: Gavin RitchieTags: postcodes, mortality, annuities

Factors

In statistical terminology, a factor is a categorisation which contains two or more mutually exclusive values called levels.  These levels may have a natural order, in which case the variable is said to be an ordinal factor. 
Written by: Stephen RichardsTags: factor, categorical factor, ordinal factor, binary factor, AIC, optimisation

How wrong could it be?

We have written previously about the importance of the independence assumption when modelling mortality for annuities and pensions. In a recent presentation to the Royal Statistical Society I showed the audience how life insurers deduplicate their annuity data and how they use postcodes to identify socio-economic status.
Written by: Stephen RichardsTags: deduplication, mortality, annuities, geodemographics, Mosaic

Playing with scales

Mortality rates increase exponentially with age.  This can make comparisons difficult, as shown in Figure 1 below, which shows the period mortality rates for males in England and Wales at ten-year intervals. 
Written by: Stephen RichardsTags: mortality, scale

Sweating your data assets

In recent years insurers have looked to making better use of the data they already have. The appeal is simple: if you have already collected the data, then it is like leaving money on the table if it is not being exploited to the full.
Written by: Stephen RichardsTags: postcodes, geodemographics, smoking, missing data, P-squared

A dip in the data pool

In the distant past, individual insurers had relatively modest business volumes and the industry needed to pool its data to get an overall data set of sufficient credibility. In the U.K., the mechanism for pooling mortality data is the CMI. An earlier blog mentioned some challenges surrounding the changing volumes of data in the CMI assured lives data set.

Written by: Stephen RichardsTags: CMI