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?'"
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 November 20
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?
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, Tang-Li