Stephen Richards

I have blogged previously about the risks in reinventing software that has already been built.  As usual, I declare my complete and utter lack of independence in the opening paragraph - I run a business providing software services to actuaries.  And while this blog might be self-interested(!), that doesn't make the point here any less true.

In an earlier blog I looked at the arguments in favour of buying in specialist software, rather than trying to build it yourself.  Of course, as someone whose business is providing software services for mortality and longevity work, I am somewhat partisan.  To balance things out, I wrote a follow-up blog on when it makes sense - even for us - to source external sof

In a previous blog I looked at how continuous-time methods can provide real-time management information.  In that example we tracked the (almost daily) development of the mortality of two tranches of new annuities, as shown again in Figure 1.

Figure 1.  Cumulative hazard, \(\hat\Lambda(t)\), for new annuities written by French insurer.  Source: Richards and Macdonald (2024).

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

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?

The R programming language has steadily increased in importance for actuaries.  A marker for this importance is that knowledge of R is required for passing UK actuarial exams.  R has many benefits, but one thing that native R lacked was an easy user interface for creating apps for others to use.  Fortunately, this has changed with the release of libraries like Shiny, which we will demonstrate here in the context of an interactive mortality tracker.

The covid-19 pandemic caused mortality shocks in many countries, and these shocks severely impact the standard forecasting models used by actuaries.  I previously showed how to robustify time-series models with a univariate index (Lee-Carter, APC) and those with a multivariate index (Cairns-Blake-Dowd, Ta