Forecasting longevity for financial applications: A first experiment with deep learning methods
| Main Author: | |
|---|---|
| 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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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 |
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2021-12-31 2021-12-31T00:00:00Z 2023-03-12T01:32:31Z |
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conference object |
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info:eu-repo/semantics/publishedVersion |
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publishedVersion |
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http://hdl.handle.net/10362/133514 |
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http://hdl.handle.net/10362/133514 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
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9783030937324 1865-0929 PURE: 41838223 https://doi.org/10.1007/978-3-030-93733-1_17 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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18 application/pdf |
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Springer International Publishing |
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Springer International Publishing |
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