Forecasting the stock market using ARIMA and ARCH/GARCH approaches

Detalhes bibliográficos
Autor(a) principal: Dinardi, Felipe Bardelli
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/10362/109749
Resumo: 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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spelling Forecasting the stock market using ARIMA and ARCH/GARCH approachesStock MarketForecastingTime SeriesARIMA modelsStock ReturnsDissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementForecasting stock returns forecasting is a crucially important topic in the study of finance, econometrics, and academic studies, and involves an in-depth study on time series. This thesis aims to examine the most representative companies on the São Paulo Stock Exchange, and based on that data, predict the behavior of future stock returns using several different forecasting methods. In time series analysis, ARIMA models are used in many situations and usually present good results; nevertheless, to determine which model best suits the data, others must be tested. When considering the high volatility of the data and factoring in the economic situation of the country that is being analyzed, other techniques must be considered, especially the ARCH family ones. Those techniques are primarily used to predict data involving Stock Markets worldwide. An accurate prediction can bring advantages for the companies who make those predictions and benefit the stakeholders directly since it provides enough information to make better decisions towards the future.Mendes, Jorge MoraisRUNDinardi, Felipe Bardelli2021-01-05T17:44:23Z2020-11-272020-11-27T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/109749TID:202572668enginfo: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:RCAAP2024-03-11T04:53:55Zoai:run.unl.pt:10362/109749Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:41:30.133261Repositório 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 the stock market using ARIMA and ARCH/GARCH approaches
title Forecasting the stock market using ARIMA and ARCH/GARCH approaches
spellingShingle Forecasting the stock market using ARIMA and ARCH/GARCH approaches
Dinardi, Felipe Bardelli
Stock Market
Forecasting
Time Series
ARIMA models
Stock Returns
title_short Forecasting the stock market using ARIMA and ARCH/GARCH approaches
title_full Forecasting the stock market using ARIMA and ARCH/GARCH approaches
title_fullStr Forecasting the stock market using ARIMA and ARCH/GARCH approaches
title_full_unstemmed Forecasting the stock market using ARIMA and ARCH/GARCH approaches
title_sort Forecasting the stock market using ARIMA and ARCH/GARCH approaches
author Dinardi, Felipe Bardelli
author_facet Dinardi, Felipe Bardelli
author_role author
dc.contributor.none.fl_str_mv Mendes, Jorge Morais
RUN
dc.contributor.author.fl_str_mv Dinardi, Felipe Bardelli
dc.subject.por.fl_str_mv Stock Market
Forecasting
Time Series
ARIMA models
Stock Returns
topic Stock Market
Forecasting
Time Series
ARIMA models
Stock Returns
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 2020
dc.date.none.fl_str_mv 2020-11-27
2020-11-27T00:00:00Z
2021-01-05T17:44:23Z
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/10362/109749
TID:202572668
url http://hdl.handle.net/10362/109749
identifier_str_mv TID:202572668
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
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