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All bases covered

It is fairly obvious by now that we are strong advocates for stochastic projection models. Such models crucially provide a basis with two components - a best-estimate force of mortality by age and year, and matching standard error values for the same 2D range.
Written by: Gavin RitchieTags: Filter information matrix by tag: mortality projections, Filter information matrix by tag: CMI

Diet? What diet?

A while back I wrote about the lower life expectancy in Scotland. This has a number of drivers, but poor diet is one of them.
Written by: Stephen RichardsTags: Filter information matrix by tag: Scotland, Filter information matrix by tag: diet

Ahead of his time

I'm giving away rather too much information about my age when I say I started work in 1990 right after graduating from university. Not long into my first job at a UK insurer, I was called to a meeting of the actuarial department.
Written by: Stephen RichardsTags: Filter information matrix by tag: Solvency II, Filter information matrix by tag: use test, Filter information matrix by tag: mortality projections

Longevity trend risk under Solvency II

Longevity trend risk is different from most other risks an insurer faces because the risk lies in the long-term trajectory taken by mortality rates. This trend unfolds over many years as an accumulation of small changes.
Written by: Stephen RichardsTags: Filter information matrix by tag: longevity risk, Filter information matrix by tag: Solvency II, Filter information matrix by tag: model risk

Steady as she goes

If you'll forgive the nautical metaphor, forecasting longevity over the past few decades has proven to be anything but plain sailing. Those plotting a course with unshakable certainty have usually ended up storm-tossed and floating in a barrel.
Written by: Gavin RitchieTags: Filter information matrix by tag: mortality projections, Filter information matrix by tag: Solvency II, Filter information matrix by tag: robustness

Why use survival models?

We and our clients much prefer to analyse mortality continuously, rather than in yearly intervals like actuaries used to do in previous centuries. Actuaries normally use μx to denote the continuous force of mortality at age x, and qx to denote the yearly rate of mortality. For any statisticians reading this, μx is the continuous-time hazard rate.
Written by: Stephen RichardsTags: Filter information matrix by tag: survival analysis, Filter information matrix by tag: survival models, Filter information matrix by tag: force of mortality, Filter information matrix by tag: hazard rate

Ahead of the curve

In an earlier post we looked at the implications for savers of the historically low interest rates in the UK. Low interest rates are a policy response to the unusual economic conditions in which the developed world currently finds itself.
Written by: Stephen RichardsTags: Filter information matrix by tag: interest, Filter information matrix by tag: yield curve

Survival models for actuarial work

The CMI recently asked for an overview note on survival models.  Since this subject is of wider actuarial interest, we wanted to make this publically available.
Written by: Stephen RichardsTags: Filter information matrix by tag: CMI, Filter information matrix by tag: survival models, Filter information matrix by tag: mortality

Muddled about middle age

Mortality statistics occasionally make the news, usually with some eye-catching statements.  Here is a recent example from the BBC: "Data from life insurance companies suggests that in the fifth and sixth decades of life you are less likely to die over the coming year than at any other time in your life."
Written by: Stephen RichardsTags: Filter information matrix by tag: mortality

A head for tails

When an insurer or reinsurer takes on a new insurance risk, there are two things of special interest: the best estimate of the risk and the tail risk.
Written by: Stephen RichardsTags: Filter information matrix by tag: tail risk, Filter information matrix by tag: Solvency II, Filter information matrix by tag: ICA