Two-Population Mortality Forecasting: An Approach Based on Model Averaging

Luca De Mori (), Pietro Millossovich, Rui Zhu and Steven Haberman
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Luca De Mori: Bayes Business School, City, University of London, London EC1Y 8TZ, UK
Pietro Millossovich: Bayes Business School, City, University of London, London EC1Y 8TZ, UK
Rui Zhu: Bayes Business School, City, University of London, London EC1Y 8TZ, UK
Steven Haberman: Bayes Business School, City, University of London, London EC1Y 8TZ, UK

Risks, 2024, vol. 12, issue 4, 1-17

Abstract: The analysis of residual life expectancy evolution at retirement age holds great importance for life insurers and pension schemes. Over the last 30 years, numerous models for forecasting mortality have been introduced, and those that allow us to predict the mortality of two or more related populations simultaneously are particularly important. Indeed, these models, in addition to improving the forecasting accuracy overall, enable evaluation of the basis risk in index-based longevity risk transfer deals. This paper implements and compares several model-averaging approaches in a two-population context. These approaches generate predictions for life expectancy and the Gini index by averaging the forecasts obtained using a set of two-population models. In order to evaluate the eventual gain of model-averaging approaches for mortality forecasting, we quantitatively compare their performance to that of the individual two-population models using a large sample of different countries and periods. The results show that, overall, model-averaging approaches are superior both in terms of mean absolute forecasting error and interval forecast accuracy.

Keywords: model averaging; mortality forecasting; two-population models; life expectancy; Gini index (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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