Forecasting longevity for financial applications: A first experiment with deep learning methods

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Forecasting longevity is essential in multiple research and policy areas, including the pricing of life insurance contracts, the valuation of capital market solutions for longevity risk management, and pension policy. This paper empirically investigates the predictive performance of Recurrent Neural Networks (RNN) with Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures in jointly modeling and multivariate time series forecasting of age-specific mortality rates at all ages. We fine-tune the three hidden layers GRU and LSTM model’s hyperparameters for time series forecasting and compare the model’s forecasting accuracy with that produced by traditional Generalised Age-Period-Cohort (GAPC) stochastic mortality models. The empirical results suggest that the two RNN architectures generally outperform the GAPC models investigated in both the male and female populations, but the results are sensitive to the accuracy criteria. The empirical results also show that the RNN-GRU network slightly outperforms the RNN with an LSTM architecture and can produce mortality schedules that capture relatively well the dynamics of mortality rates across age and time. Further investigations considering other RNN architectures, calibration procedures, and sample datasets are necessary to confirm the superiority of RNN in forecasting longevity.
Original languageEnglish
Title of host publicationMachine Learning and Principles and Practice of Knowledge Discovery in Databases
Subtitle of host publicationInternational Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part II
PublisherSpringer International Publishing
Pages232-249
Number of pages18
ISBN (Print)9783030937324
DOIs
Publication statusPublished - 31 Dec 2021
Event21st European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021 - Virtual, Online
Duration: 13 Sep 202117 Sep 2021

Publication series

NameCommunications in Computer and Information Science
Volume1525 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference21st European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021
Period13/09/2117/09/21

Keywords

  • Mortality forecasting
  • RNN
  • LSTM
  • GRU
  • Deep learning
  • Pensions
  • Insurance

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