A factor augmented vector autoregressive model and a stacked de-noising auto-encoders forecast combination to predict the price of oil

Detalhes bibliográficos
Autor(a) principal: Parravicini, Giovanni
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/73196
Resumo: The following dissertation aims to show the benefits of a forecast combination between an econometric and a deep learning approach. On one side, a Factor Augmented Vector Autoregressive Model (FAVAR) with naming variables identification following Stock and Watson (2016)1; on the other side, a Stacked De-noising Auto-Encoder with Bagging (SDAE-B) following Zhao, Li and Yu (2017)2 are implemented. From January 2010 to September 2018 Two-hundred-eighty-one monthly series are used to predict the price of the West Texas Intermediate (WTI). The model performance is analysed by Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and Directional Accuracy (DA). The combination benefits from both SDAE-B’s high accuracy and FAVAR’s interpretation features through impulse response functions (IRFs) and forecast error variance decomposition (FEVD).
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spelling A factor augmented vector autoregressive model and a stacked de-noising auto-encoders forecast combination to predict the price of oilOil industryFAVARSDAE-B implementationDomínio/Área Científica::Ciências Sociais::Economia e GestãoThe following dissertation aims to show the benefits of a forecast combination between an econometric and a deep learning approach. On one side, a Factor Augmented Vector Autoregressive Model (FAVAR) with naming variables identification following Stock and Watson (2016)1; on the other side, a Stacked De-noising Auto-Encoder with Bagging (SDAE-B) following Zhao, Li and Yu (2017)2 are implemented. From January 2010 to September 2018 Two-hundred-eighty-one monthly series are used to predict the price of the West Texas Intermediate (WTI). The model performance is analysed by Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and Directional Accuracy (DA). The combination benefits from both SDAE-B’s high accuracy and FAVAR’s interpretation features through impulse response functions (IRFs) and forecast error variance decomposition (FEVD).Francesco, FrancoPereira, Pedro VallsRUNParravicini, Giovanni2020-06-30T00:30:48Z2019-01-242019-01-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/73196TID:202225682enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:33:54Zoai:run.unl.pt:10362/73196Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:35:17.987475Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A factor augmented vector autoregressive model and a stacked de-noising auto-encoders forecast combination to predict the price of oil
title A factor augmented vector autoregressive model and a stacked de-noising auto-encoders forecast combination to predict the price of oil
spellingShingle A factor augmented vector autoregressive model and a stacked de-noising auto-encoders forecast combination to predict the price of oil
Parravicini, Giovanni
Oil industry
FAVAR
SDAE-B implementation
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short A factor augmented vector autoregressive model and a stacked de-noising auto-encoders forecast combination to predict the price of oil
title_full A factor augmented vector autoregressive model and a stacked de-noising auto-encoders forecast combination to predict the price of oil
title_fullStr A factor augmented vector autoregressive model and a stacked de-noising auto-encoders forecast combination to predict the price of oil
title_full_unstemmed A factor augmented vector autoregressive model and a stacked de-noising auto-encoders forecast combination to predict the price of oil
title_sort A factor augmented vector autoregressive model and a stacked de-noising auto-encoders forecast combination to predict the price of oil
author Parravicini, Giovanni
author_facet Parravicini, Giovanni
author_role author
dc.contributor.none.fl_str_mv Francesco, Franco
Pereira, Pedro Valls
RUN
dc.contributor.author.fl_str_mv Parravicini, Giovanni
dc.subject.por.fl_str_mv Oil industry
FAVAR
SDAE-B implementation
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Oil industry
FAVAR
SDAE-B implementation
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description The following dissertation aims to show the benefits of a forecast combination between an econometric and a deep learning approach. On one side, a Factor Augmented Vector Autoregressive Model (FAVAR) with naming variables identification following Stock and Watson (2016)1; on the other side, a Stacked De-noising Auto-Encoder with Bagging (SDAE-B) following Zhao, Li and Yu (2017)2 are implemented. From January 2010 to September 2018 Two-hundred-eighty-one monthly series are used to predict the price of the West Texas Intermediate (WTI). The model performance is analysed by Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and Directional Accuracy (DA). The combination benefits from both SDAE-B’s high accuracy and FAVAR’s interpretation features through impulse response functions (IRFs) and forecast error variance decomposition (FEVD).
publishDate 2019
dc.date.none.fl_str_mv 2019-01-24
2019-01-24T00:00:00Z
2020-06-30T00:30:48Z
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/10362/73196
TID:202225682
url http://hdl.handle.net/10362/73196
identifier_str_mv TID:202225682
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:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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