A Deep Learning integrated mortality model for Longevity Swap pricing

Bibliographic Details
Main Author: Salvo, Alberto Di
Publication Date: 2022
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/145541
Summary: Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management
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network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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spelling A Deep Learning integrated mortality model for Longevity Swap pricingMortalityDeep LearningLong short-term memoryGated Recurrent UnitLee-Carter modelLongevity riskLongevity swapLongevity swap pricingDissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementThis research empirically investigates the usage of Recurrent Neural Networks (RNN) to improve the accuracy of mortality rates forecasting within the context of Longevity linked securities pricing. The benchmark model in the mortality field is the classical Lee-Carter; the forecasting procedure of these model is often conducted with ARIMA models. I consider a fixed forecasting time horizon in order to compare the performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) with different hyperparameter and data input choices against that produced by the best fitted ARIMA models. The results are then applied to Longevity Swap pricing in order to better estimates the premium of the derivatives contracts. The investigation is conducted for six countries, using mortality data from 1950 onwards, differentiating by gender. The research shows how RNN outperform the classical ARIMA models in the forecasting procedure. Although the advantages of RNN’s techniques are strictly bounded to the set of hyperparameter used for the comparison; the outcomes of such approaches can vary greatly using different input choices. In the end the results shows that an RNN approach can bring significant changes to the price of Longevity Linked securities. The research is in the first place one of the few to test the forecasting accuracy of Deep Learning methods accounting for alternative methodological, hyperparameter and data input choices. Afterwards the investigation demonstrate the necessity of revisit the classical mortality models in order to better estimates prices of derivatives contracts that are very useful in the context of Longevity risk.Bravo, Jorge Miguel VenturaRUNSalvo, Alberto Di2022-11-15T18:55:51Z2022-10-252022-10-25T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/145541TID:203126130enginfo: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:RCAAP2024-05-22T18:06:43Zoai:run.unl.pt:10362/145541Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:37:13.016154Repositórios 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 A Deep Learning integrated mortality model for Longevity Swap pricing
title A Deep Learning integrated mortality model for Longevity Swap pricing
spellingShingle A Deep Learning integrated mortality model for Longevity Swap pricing
Salvo, Alberto Di
Mortality
Deep Learning
Long short-term memory
Gated Recurrent Unit
Lee-Carter model
Longevity risk
Longevity swap
Longevity swap pricing
title_short A Deep Learning integrated mortality model for Longevity Swap pricing
title_full A Deep Learning integrated mortality model for Longevity Swap pricing
title_fullStr A Deep Learning integrated mortality model for Longevity Swap pricing
title_full_unstemmed A Deep Learning integrated mortality model for Longevity Swap pricing
title_sort A Deep Learning integrated mortality model for Longevity Swap pricing
author Salvo, Alberto Di
author_facet Salvo, Alberto Di
author_role author
dc.contributor.none.fl_str_mv Bravo, Jorge Miguel Ventura
RUN
dc.contributor.author.fl_str_mv Salvo, Alberto Di
dc.subject.por.fl_str_mv Mortality
Deep Learning
Long short-term memory
Gated Recurrent Unit
Lee-Carter model
Longevity risk
Longevity swap
Longevity swap pricing
topic Mortality
Deep Learning
Long short-term memory
Gated Recurrent Unit
Lee-Carter model
Longevity risk
Longevity swap
Longevity swap pricing
description Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management
publishDate 2022
dc.date.none.fl_str_mv 2022-11-15T18:55:51Z
2022-10-25
2022-10-25T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/145541
TID:203126130
url http://hdl.handle.net/10362/145541
identifier_str_mv TID:203126130
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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
_version_ 1833596839260061696

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