Best practice in mortality work - regulatory comments

In a letter to the Chief Actuaries of UK insurance businesses, Malik (2019) highlighted two aspects of what the regulator regards as good practice in mortality work:

  1. Considering trends in mortality in conjunction with seasonal variation, especially excess winter mortality, and
  2. Considering how far into the future it is reasonable to project a cohort effect.

Seasonal variation in mortality occurs in all countries and affects insured portfolios as much as the wider population. Seasonal variation also has a greater impact as age increases. The point that Malik (2019) is making is that any estimate of recent trends will be impacted by whether the start and end points of the analysis were years of weaker or heavier seasonal mortality. If the starting point were a season of lighter mortality and the end point a season of higher seasonal mortality, then an underlying trend of mortality improvement could be masked. Indeed, the shorter the period, the more influence seasonal variation can have. One option is to jointly model time trends and seasonal effects, ideally also allowing for other risk factors; Richards (2019) presents a model that can estimate both the age-related time trend and seasonal effects in a portfolio, and gives results for a medium-sized Scottish pension scheme.

Cohort effects are also a frequent topic here; see for example our paper on the APCI model (Richards et al, 2017), where we point out the unsuitability of any kind of smoothing of cohort effects. Other matters include dealing with cohort effects for closed portfolios and the treatment of corner cohorts with few observations. However, a more fundamental question is whether cohort effects are as strong as they historically appeared to be, as covered in Alexandre Boumezoued's recent blog. Here the data source is key their revised data protocols make the Human Mortality Database (HMD) the best place to source population data for modelling cohort effects.


Malik, S. (2019) Observations from recent regulatory reviews, 19 June 2019, Bank of England.

Richards, S. J., Currie, I. D., Kleinow, T. and Ritchie, G. P. (2019) A stochastic implementation of the APCI model for mortality projections, British Actuarial Journal, Volume 24, 2019, e13. DOI:

Richards, S. J. (2019) A Hermite-spline model of post-retirement mortality, Scandinavian Actuarial Journal, DOI: 10.1080/03461238.2019.1642239.




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Stephen Richards
Stephen Richards is the Managing Director of Longevitas
Seasonal patterns in Longevitas

Longevitas supports two methods of modelling seasonal patterns:

  1. The CalendarPeriod variable, and
  2. The SeasonalEffect variable.

Longevitas users can fit models with a variety of period effects using the CalendarPeriod variable. Simply go to the Configuration section and enable this in the Modelling tab. There you will also have the option to select the frequency of effects, as well as their alignment during the year. The CalendarPeriod is a categorical variable, i.e. the effect is assumed to be constant within the period.

The SeasonalEffect variable is a feature of the Hermite family of models. It is a continuous variable, and so is a more parsimonious option when modelling post-retirement mortality differentials.