Stephen Richards

We have written extensively about how to use postcodes for mortality modelling. The best approach in the UK is to use so-called geodemographic profilers, which map postcodes to relatively homogeneous groups of households sharing certain socio-economic characteristics.

In an earlier post I mentioned the advent of survivor forwards, or S-forwards, a derivative contract which could be used for hedging pension liabilities.

Actuaries make great use of so-called standard tables.  These are annual probabilities at each whole age for males and females.  However, often mortality rates are required at ages which are not whole numbers. 

In civil engineering, no building can be sounder than the foundation on which it rests.  A similar comment applies to statistical analysis, which is obviously limited by the quality of the underlying data. 

Is there a limit to life expectancy?

I have written several times about the challenges in creating mortality projections based on cause-of-death data.  Those interested in the details can consult my recent paper published in a special edition of the British Actuarial Journal. 

I wrote earlier about survivor forwards as a means of transferring longevity risk.  One natural question for investors to ask is: what is the likelihood of loss exceeding a given amount?

Mortality improvements can be analysed in a number of ways.  A common desire is to want to separate mortality improvements into components for period and cohort. 

In a recent meeting I was asked by a reinsurer what the advantages were of using statistical models in his business. The reinsurer knew about the greater analytical power of survival models, but he wanted more.

I have written before on aspects of the CMI's new deterministic projection model. One hoped-for goal was that the CMI 2010 model would become a "common currency" for communicating mortality-improvement bases.