Information Matrix

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

In earlier blogs I discussed two techniques for handling outliers in mortality forecasting models:

• Chen & Liu (1993) for ARIMA models for univariate time series, and

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

I recently came across an online article by W. S. Warren, the deputy editor of Science Advances.  In the article Warren outlines some easy ways for submitting authors to improve their paper's chances of being accepted for journal publication.

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