Information Matrix

The sooner you know about a problem, the sooner you can do something about it.  I have written before about real-time updates to mortality estimates during shocks.  However, real-time methods also have application to everyday management questions.  Consider Figure 1(a), which shows a surge in new annuities in December 2014.  The volume of new annuities written in that month was large enough to shift the average age of the in-force annuit

Actuaries tasked with analysing a portfolio's mortality experience face a gap between what has happened in the outside world and the data they actually work with.  The various difference levels are depicted in Figure 1.

Figure 1.  The actuarial data onion.

Last week I presented at the Longevity 18 conference.  My topic was on robustifying stochastic mortality models when the calibrating data contain outliers, such as caused by the COVID-19 pandemic.  A copy of the presentation can be downloaded here, which is based on a paper to be presented at an IFoA sessional meeting in N

You are in charge of systems programming for an insurer writing disability insurance.  It is your job to write reporting modules to meet the needs of the actuaries, claims managers, accountants and so on.  Where to start?

The data would seem to be a good place.  I'll take it as read what kind of data the business will generate.  The question is how to represent it for efficient use in our programs - something we worry about so that the user doesn't have to.

All governments like to divert attention from their mistakes.  However, this is tricky in an open democracy with a free press if those mistakes lead to tens of thousands of deaths.  In contrast, it is straightforward for an authoritarian regime to manipulate the death counts.  Nevertheless, it is hard to hide all the indirect consequences of excess deaths.  This allows resourceful researchers to uncover what even dictatorships would rather keep hidden.  In this blog we look at examples in China and Russia.

in Kleinow & Richards (2016, Table 5) we noted a seeming conundrum: the best-fitting ARIMA model for the time index in a Lee-Carter model also produced much higher value-at-risk (VaR) capital requirements for longevity trend risk.  How could this be?