In Richards et al (2013) we described how actuaries can create mortality tables derived from a portfolio's own experience, rather than relying on tables published elsewhere. There are good reasons why actuaries need to be able to do this, and we came across a stark reminder of this while writing Richards & Macdonald (2024).
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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).
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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
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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.
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Scene: A meeting room, London, c.1997. Two actuaries are contemplating a flipchart on which is displayed some mathematics, including a double integral.
Actuary 1: "That's the kind of thing a Danish actuary would understand.'"
Actuary 2: "Yes, but could they calculate a premium rate?'"
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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.
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Pricing block transactions is a high-stakes business. An insurer writing a bulk annuity has one chance to assess the price to charge for taking on pension liabilities. There is a lot to consider, but at least there is data to work with: for the economic assumptions like interest rates and inflation, the insurer has market prices. For the mortality basis, the insurer usually gets several years of mortality-experience data from the pensi
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In the course of a recent investigation, with my colleagues Dr Oytun Haçarız and Professor Torsten Kleinow, a key parameter was the mortality rate of persons suffering from Hypertrophic Cardiomyopathy (HCM), an inherited heart disorder characterized by thickening of the left ventricular muscle wall. It is quite rare, so precision is not to be expected, and indeed an annual mortality rate of 1% \((q_x=0.01)\), independent of age \(x\), is widely cited. I
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