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

Bibliographic Details
Main Author: Bravo, Jorge M.
Publication Date: 2021
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/133514
Summary: Bravo J.M. (2021) Forecasting Longevity for Financial Applications: A First Experiment with Deep Learning Methods. In: Kamp M. et al. (eds) Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. Communications in Computer and Information Science, vol 1525. Springer, Cham. https://doi.org/10.1007/978-3-030-93733-1_17
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network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository_id_str https://opendoar.ac.uk/repository/7160
spelling Forecasting longevity for financial applications: A first experiment with deep learning methodsMortality forecastingRNNLSTMGRUDeep learningPensionsInsuranceComputer Science(all)Mathematics(all)Bravo J.M. (2021) Forecasting Longevity for Financial Applications: A First Experiment with Deep Learning Methods. In: Kamp M. et al. (eds) Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. Communications in Computer and Information Science, vol 1525. Springer, Cham. https://doi.org/10.1007/978-3-030-93733-1_17Forecasting 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.Springer International PublishingInformation Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNBravo, Jorge M.2023-03-12T01:32:31Z2021-12-312021-12-31T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion18application/pdfhttp://hdl.handle.net/10362/133514eng97830309373241865-0929PURE: 41838223https://doi.org/10.1007/978-3-030-93733-1_17info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:59:48Zoai:run.unl.pt:10362/133514Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:30:44.543741Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Forecasting longevity for financial applications: A first experiment with deep learning methods
title Forecasting longevity for financial applications: A first experiment with deep learning methods
spellingShingle Forecasting longevity for financial applications: A first experiment with deep learning methods
Bravo, Jorge M.
Mortality forecasting
RNN
LSTM
GRU
Deep learning
Pensions
Insurance
Computer Science(all)
Mathematics(all)
title_short Forecasting longevity for financial applications: A first experiment with deep learning methods
title_full Forecasting longevity for financial applications: A first experiment with deep learning methods
title_fullStr Forecasting longevity for financial applications: A first experiment with deep learning methods
title_full_unstemmed Forecasting longevity for financial applications: A first experiment with deep learning methods
title_sort Forecasting longevity for financial applications: A first experiment with deep learning methods
author Bravo, Jorge M.
author_facet Bravo, Jorge M.
author_role author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Bravo, Jorge M.
dc.subject.por.fl_str_mv Mortality forecasting
RNN
LSTM
GRU
Deep learning
Pensions
Insurance
Computer Science(all)
Mathematics(all)
topic Mortality forecasting
RNN
LSTM
GRU
Deep learning
Pensions
Insurance
Computer Science(all)
Mathematics(all)
description Bravo J.M. (2021) Forecasting Longevity for Financial Applications: A First Experiment with Deep Learning Methods. In: Kamp M. et al. (eds) Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. Communications in Computer and Information Science, vol 1525. Springer, Cham. https://doi.org/10.1007/978-3-030-93733-1_17
publishDate 2021
dc.date.none.fl_str_mv 2021-12-31
2021-12-31T00:00:00Z
2023-03-12T01:32:31Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/133514
url http://hdl.handle.net/10362/133514
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 9783030937324
1865-0929
PURE: 41838223
https://doi.org/10.1007/978-3-030-93733-1_17
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 18
application/pdf
dc.publisher.none.fl_str_mv Springer International Publishing
publisher.none.fl_str_mv Springer International Publishing
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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