Information Matrix

Filter

Posts feed
Publication date

Fifteen-year (h)itch

Effective risk modelling is about grouping people with shared characteristics which affect this risk.  In mortality analysis by far the most important risk factor is age, so it is not a good idea to mix the young and old if it can be avoided.  By way of illustration, Figure 1 shows that mortality rates increase exponentially over much of the post-retirement age range. 

Written by: Stephen RichardsTags: survival analysis, survival curve, curve of deaths

Top of the table

In an earlier post we also showed how the U.K. was top of the obesity league amongst major EU nations.   Happily, the U.K. is top of a more constructive EU league table, namely the (lack of) affordability of cigarettes.
Written by: Stephen RichardsTags: smoking

Open verdict

If any doubt about Linux and Open Source technologies existed in Enterprise IT departments it must surely have been erased by last week's news: The London Stock Exchange, one of the engines that propelled the UK to the top of the World Economic Forum rankings, has invested in a Linux trading platform.
Written by: Gavin RitchieTags: open source, technology

Part of the story

The Institute of Actuaries' sessional meeting on 28th September 2009 discussed an interesting paper.  It covered similar material to that in Richards (2008), but used different methods and different data.
Written by: Stephen Richards

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

A Scottish question

The Scots are an innovative bunch, including the inventor of the telephone and the discoverer of penicillin.
Written by: Stephen RichardsTags: Scotland, region, geodemographics

Out for the count

In an earlier post we described a problem when fitting GLMs for qx over multiple years.  The key mistake is to divide up the period over which the individual was observed in a model for individual mortality. 
Written by: Stephen RichardsTags: survival models, force of mortality, GLM, missing data

Stabilising projections

With many stochastic models of mortality, projections of future mortality rates are done using a time series.  In a landmark paper, Currie, Durban and Eilers (2004) introduced the idea of using P-splines as an alternative means of generating a forecast.  P-splines formed the basis of a projection tool the CMI made fr

Written by: Stephen RichardsTags: P-splines, time series, mortality projections, CMI

Partial buy-outs

It is quite common for a pension scheme to want to reduce its risk, but to be unable to afford a full buy-out.  The question is how best to reduce risk with the funds available, i.e. which liabilities to buy out first. 
Written by: Stephen RichardsTags: buy-out, concentration risk, trend risk, tail risk