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

The Institute and Faculty of Actuaries (IFoA) is hosting a meeting on robust mortality forecasting in the presence of outliers on 27th November 2023.  A copy of the draft paper and booking details for attendance can be found on the IFoA website.

2.8.7 update

• A new resource area Training tab including datasets and exercises.
• Availability of 2D Period shocks model as per Kirkby & Currie (2010).
• Availability of a CBD M9 model with smoothing on main age term.
• New plot shows the inverse roots of the characteristic polynomials of the autoregressive (AR) and moving-average (MA) parts of the ARIMA model.
• License configurable SAML-based single-sign-on authentication.
• Platform, security and administrative support updates.
• Various fixes

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 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

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

In mortality forecasting work we often deal with downward trends.  It is often tempting to jump to the assumption of a linear trend, in part because this makes for easier mathematics.  However, real-world phenomena are rarely purely linear, and the late Iain Currie advocated linear adjustment as means of judging linear-seeming patterns.  This involves calculating a line between the first and last points, and deducting the line value at each data point t

Longevitas Ltd is pleased to announce the production release of v2.8.6 of the Projections Toolkit. This version contains powerful productivity and ease-of-use features including: