Stephen Richards

Last year Iain wrote about a smooth model to identify mortality shocks, using Swedish population data to illustrate the impact of the 1918 influenza pandemic. 

One of the most written-about models for stochastic mortality projections is that from Lee & Carter (1992). 

A particular leitmotif of 2010 is productivity — getting more work done with the time and resources available.  Often this is about controlling costs, but in the insurance sector in the European Union it is also about adapting to scarcity of resources: with Solvency II looming, there is strong Europe-wide demand for actuarial expertise.

We have often written about how modelling the force of mortality, μx, is superior to using the rate of mortality, qx.

Trends in cause of death can be an instructive way of looking at past mortality, although we have previously seen that we have to be very careful that an apparent "trend" is not due to changes in recording.  Leaving aside the problems of shifting classification over time, what of the categories themselves?

When projecting mortality rates it is common for people to ask what sort of changes in causes of death might be required to achieve a particular scenario.  Often one is asked to posit what causes of death have to be "eliminated", and the results can lead to the conclusion that a particular projection is unlikely and therefore too prudent.

We have written before about how survival models make better use of available data. 

Previously I wrote about how mortality rates by cause of death vary by deprivation index (and, by implication, socio-economic group). This substantially complicates any attempt to use cause-of-death data to make projections of mortality for annuity portfolios and defined-benefit pension schemes.

Last night an paper on the management of annuities was presented to the Faculty of Actuaries in Edinburgh.

Boxing Clever