Information Matrix

Filter

Posts feed
Publication date

The bottom line

At it's core, the study of mortality is based on a simple ratio — the number of deaths, D, divided by the population exposed to the risk of death, E:

mortality rate = D / E

Written by: Stephen RichardsTags: mortality projections, Japan

History lessons

In the debate about how fast mortality will improve in the future, sometimes it is useful to remind ourselves how far we have come. 
Written by: Stephen RichardsTags: mortality projections

Cutting the bias

With the exception of dressmaking, bias is generally undesirable. This is particularly the case when projecting future mortality rates for reserving for pension liabilities. 
Written by: Stephen RichardsTags: cause of death, mortality projections

Cast adrift

One of the most written-about models for stochastic mortality projections is that from Lee & Carter (1992). 
Written by: Stephen RichardsTags: mortality projections, Lee-Carter, drift model, ARIMA

Getting the rough with the smooth

There are two fundamentally different ways of thinking about how mortality evolves over time: (a) think of mortality as a time series (the approach of the Lee-Carter model and its generalizations in the Cairns-Blake-Dowd family); (b) think of mortality as a smooth surface (the approach of the 2D P-spline models of Currie, Durban and Eilers and the smooth versions of the Lee-Carter model).
Written by: Iain CurrieTags: mortality projections, simulation

The cost of uncertainty

In an earlier blog I wrote about how stochastic volatility in run-off increases with age. This applies when you exactly know (or think you know) the current and future mortality rates.
Written by: Stephen RichardsTags: mortality projections, ICA, Solvency II, matching

Over-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?
Written by: Iain CurrieTags: over-dispersion, duplicates, mortality projections, ICA, Solvency II

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

Written by: Iain CurrieTags: over-dispersion, mortality projections, mortality improvements

Back(test) to the future

Stochastic projections of future mortality are increasingly used not just to set future best-estimates, but also to inform on stress tests such as for ICAs in the UK.  By the time the Solvency II regime comes into force, I expect most major insurers across the EU will be using stochastic models for mortality projections (if they are not already doing so).

Written by: Stephen RichardsTags: mortality projections, ICA, Solvency II, Lee-Carter, CMIR17, back-test

Forecasting with limited portfolio data

In a recent post on basis risk in mortality projections, I floated the idea of forecasting with limited data and even suggested that it would be possible to use the method to produce a family of consistent forecasts for different classes of business. The present post describes an example of how this idea works in practice.
Written by: Iain CurrieTags: basis risk, mortality projections