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Posts feedReporting delays
When performing a mortality analysis, it is my practice to disregard the most recent six months or so of experience data. The reason is delays in the reporting and recording of deaths, i.e. occurred-but-not-reported (OBNR) to use the terminology of Lawless (1994). We use the term OBNR, rather than the more familiar term IBNR (incurred-but-not-reported); IBNR is associated with "cost-orientated" delay distributions of insurance claims (Jewell, 1989), whereas we are focused on just the delay itself.
Visualising data-quality in time
Visualising covid-19 in experience data
As 2020 edges to a close, life-office actuaries need to set mortality bases for year-end valuations. An obvious question is what impact the covid-19 pandemic has had on the mortality experience of their portfolio? One problem is that traditional actuarial analysis was often done on the basis of annual rates, whereas the initial covid-19 shock was delivered over a period of a couple of months in early 2020 in Europe.
Going negative
Continuous improvement
In a previous blog I demonstrated that there was a statistically significant relationship between pension size and mortality. In a subsequent blog I looked at the improvements in model fit from treating pension size as a factor, but concluded that this was only a partial solution. In practice actuaries would prefer to avoid the discretisation error that com
Mortality patterns in time
The COVID-19 pandemic has created strong interest in mortality patterns in time, especially mortality shocks. Actuaries now have to consider the effect of such shocks in their portfolio data, and in this blog we consider a non-parametric method of doing this.
Pension size as a factor
Mortality and pension size
It will surprise nobody reading this blog that richer people tend to live longer. This applies both between countries (countries with a higher per capita income tend to have higher life expectancies) and also within countries (people of higher socio-economic status tend to live longer than others, even when they all share the same comprehensive healthcare system).
Turning the tables
Modelling improvements in experience data - I
In the first of a pair of blogs we will look at how to allow for changes in mortality levels when calibrating models to experience analysis. We start with time-varying extensions of traditional parametric models proposed by actuaries, beginning of course with the Gompertz (1825) model:
\[{\rm Gompertz}: \mu_{x,y} = e^{\alpha+\beta x + \delta(y-2000)}\qquad (1)\]