Information Matrix

If you are in the business of pricing and managing longevity risk, you need software to help you perform your analysis. You have two choices:

A large part of our service has traditionally revolved around Dedicated Servers — parallelised instances of our applications running on multi-threaded platforms for a single license holder (in contrast our shared servers offer single-thread performance in a multi-tenant way to multiple license holders, a model that is suitable for only the least demanding use-cases).

Epidemics and pandemics are, by definition, fast-moving and difficult to track. These are the diseases that we couldn't keep a lid on, outbreaks that breached our initial efforts at control. It follows then, that ongoing reporting of such diseases won't be entirely accurate, subject to various limitations imposed by testing and recording protocols. This reality is misused by some who believe that reported impacts are exaggerated and societal responses unjustified, but such a belief runs counter to the evidence.

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.