Information Matrix

In a previous blog I outlined my six-month rule of thumb for discarding mortality experience affected by reporting delays. However, this can be awkward where there is a hard limit on how far back the experience data goes. For example, when a pension scheme switches administrator, or an insurer migrates business from one system to another, past mortality data is usually the first casualty.

In his recent blog Stephen described some empirical evidence in support of his practice of discarding the most recent six months' data, to reduce the effect of delays in reporting deaths. This blog demonstrates that the practice can also be justified theoretically in the survival modelling framework, although the choice of six months as the cut-off remains an empirical matter.

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.

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.

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

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.