Title: Modelling longevity risk. A practical study of the effect of statistical pre-adjustments on mortality trend forecasts

Authors: Alexandros E. Milionis; Nikolaos G. Galanopoulos; Peter Hatzopoulos; Aliki Sagianou

Addresses: Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, Greece ' Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, Greece ' Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, Greece ' Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, Greece

Abstract: An important risk in the actuarial industry is the longevity risk, therefore the as accurate as possible prediction of mortality rates is very crucial. Such predictions are performed by modelling the mortality rates using mortality models and predicting the future mortality trends. Aiming at possible improvements of such forecasts, we examine the effect of data transformation-'linearisation' on the quality of time series forecasts of mortality, using data resulted from mortality models for England-Wales. By time series 'linearisation' is meant the treatment of causes that disrupt the underlying stochastic process. Results indicate a clear improvement for interval forecasts of mortality. However, the result for point forecasts is not as clear. The documented improvement in interval forecasts can significantly affect the solvency capital requirement, rendering some pension providers at a competitive advantage. It was also confirmed that the transformed-linearised series satisfy better the need for normality as compared to the original series.

Keywords: longevity risk; applied time series analysis; time series transformation-'linearisation'; actuarial time series forecasts; mortality rates.

DOI: 10.1504/IJCEE.2026.151010

International Journal of Computational Economics and Econometrics, 2026 Vol.16 No.1/2, pp.137 - 159

Accepted: 09 Jul 2025
Published online: 08 Jan 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article