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A random forest algorithm to improve the Lee–Carter mortality forecasting: impact on q-forward

  • Susanna LevantesiEmail author
  • Andrea Nigri
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Abstract

Increased life expectancy in developed countries has led researchers to pay more attention to mortality projection to anticipate changes in mortality rates. Following the scheme proposed in Deprez et al. (Eur Actuar J 7(2):337–352, 2017) and extended by Levantesi and Pizzorusso (Risks 7(1):26, 2019), we propose a novel approach based on the combination of random forest and two-dimensional P-spline, allowing for accurate mortality forecasting. This approach firstly provides a diagnosis of the limits of the Lee–Carter mortality model through the application of the random forest estimator to the ratio between the observed deaths and their estimated values given by a certain model, while the two-dimensional P-spline are used to smooth and project the random forest estimator in the forecasting phase. Further considerations are devoted to assessing the demographic consistency of the results. The model accuracy is evaluated by an out-of-sample test. Finally, we analyze the impact of our model on the pricing of q-forward contracts. All the analyses have been carried out on several countries by using data from the Human Mortality Database and considering the Lee–Carter model.

Keywords

Mortality Machine learning Two-dimensional P-spline q-Forward 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of StatisticsSapienza University of RomeRomeItaly

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