Forecasting stock index volatility - a comparison of models

Detalhes bibliográficos
Autor(a) principal: Gemst, Marion Van
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/107453
Resumo: This thesis explores the useof popularmachine learning algorithms(K-Nearest NeighborandRandom Forest)and compares them to traditional techniques (Random Walk, ARIMAand GARCH) for forecastingone-day, one-week, one-monthand one-quarter volatilityusingThe OsloStock ExchangeAll Share Index. A number of error metrics are applied(RMSE, MAE, MAPE and R-squared)in order to compare their results.Machine learning methods are shown to forecast thechanges in volatilityto some extent, however, evidence isfound favouringtheARIMAmodel when forecastingvolatility time series.
id RCAP_f6ab3fab4acc770325ac44e6b0651838
oai_identifier_str oai:run.unl.pt:10362/107453
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Forecasting stock index volatility - a comparison of modelsVolatilityForecastingGarchMachine learningDomínio/Área Científica::Ciências Sociais::Economia e GestãoThis thesis explores the useof popularmachine learning algorithms(K-Nearest NeighborandRandom Forest)and compares them to traditional techniques (Random Walk, ARIMAand GARCH) for forecastingone-day, one-week, one-monthand one-quarter volatilityusingThe OsloStock ExchangeAll Share Index. A number of error metrics are applied(RMSE, MAE, MAPE and R-squared)in order to compare their results.Machine learning methods are shown to forecast thechanges in volatilityto some extent, however, evidence isfound favouringtheARIMAmodel when forecastingvolatility time series.Silva, André CastroRUNGemst, Marion Van2021-01-03T01:30:36Z2020-01-162020-01-032020-01-16T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/107453TID:202523900enginfo: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:RCAAP2024-03-11T04:52:12Zoai:run.unl.pt:10362/107453Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:40:59.440348Repositório 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 Forecasting stock index volatility - a comparison of models
title Forecasting stock index volatility - a comparison of models
spellingShingle Forecasting stock index volatility - a comparison of models
Gemst, Marion Van
Volatility
Forecasting
Garch
Machine learning
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short Forecasting stock index volatility - a comparison of models
title_full Forecasting stock index volatility - a comparison of models
title_fullStr Forecasting stock index volatility - a comparison of models
title_full_unstemmed Forecasting stock index volatility - a comparison of models
title_sort Forecasting stock index volatility - a comparison of models
author Gemst, Marion Van
author_facet Gemst, Marion Van
author_role author
dc.contributor.none.fl_str_mv Silva, André Castro
RUN
dc.contributor.author.fl_str_mv Gemst, Marion Van
dc.subject.por.fl_str_mv Volatility
Forecasting
Garch
Machine learning
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Volatility
Forecasting
Garch
Machine learning
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description This thesis explores the useof popularmachine learning algorithms(K-Nearest NeighborandRandom Forest)and compares them to traditional techniques (Random Walk, ARIMAand GARCH) for forecastingone-day, one-week, one-monthand one-quarter volatilityusingThe OsloStock ExchangeAll Share Index. A number of error metrics are applied(RMSE, MAE, MAPE and R-squared)in order to compare their results.Machine learning methods are shown to forecast thechanges in volatilityto some extent, however, evidence isfound favouringtheARIMAmodel when forecastingvolatility time series.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-16
2020-01-03
2020-01-16T00:00:00Z
2021-01-03T01:30:36Z
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/10362/107453
TID:202523900
url http://hdl.handle.net/10362/107453
identifier_str_mv TID:202523900
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
_version_ 1799138023315603456

Registros relacionados