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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One interesting aspect of maximum-likelihood estimation is the common behaviour of estimators, regardless of the nature of the data and model. Recall that the maximum-likelihood estimate, \(\hat\theta\), is the value of a parameter \(\theta\) that maximises the likelihood function, \(L(\theta)\), or the log-likelihood function, \(\ell(\theta)=\log L(\theta)\). By way of example, consider the following three single-parameter distributions:
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Longevitas Ltd is pleased to announce the production release of v2.8.6 of its Longevitas risk-modelling software.
Longevitas Ltd is pleased to announce the production release of v2.8.4 of its Longevitas risk-modelling software.
