Realized multivariate GARCH with factors

Detalhes bibliográficos
Autor(a) principal: Coelho, Murilo Getlinger
Data de Publicação: 2022
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
Texto Completo: https://hdl.handle.net/10438/33079
Resumo: A previsão dos segundos momentos dos retornos de ativos é essencial na seleção de portfólio. Em um ambiente multivariado, a dimensionalidade do problema e a precisão das previsões são as principais preocupações. Propomos uma nova metodologia para prever matrizes de covariância unindo duas abordagens existentes na literatura: dados intradiários para aumentar a acurácia preditiva e fatores para reduzir a dimensionalidade. Assumimos um modelo GARCH realizado multivariado para os fatores e um conjunto de modelos GARCH multivariados realizados entre cada ação e os fatores. Comparamos nossa metodologia empíricamente com a literatura padrão, otimizando um portfólio no universo de ações do S&P500.
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spelling Coelho, Murilo GetlingerEscolas::EESPLunde, AsgerMedeiros, Marcelo CunhaFernandes, Marcelo2023-01-09T15:52:59Z2023-01-09T15:52:59Z2022-12-13https://hdl.handle.net/10438/33079A previsão dos segundos momentos dos retornos de ativos é essencial na seleção de portfólio. Em um ambiente multivariado, a dimensionalidade do problema e a precisão das previsões são as principais preocupações. Propomos uma nova metodologia para prever matrizes de covariância unindo duas abordagens existentes na literatura: dados intradiários para aumentar a acurácia preditiva e fatores para reduzir a dimensionalidade. Assumimos um modelo GARCH realizado multivariado para os fatores e um conjunto de modelos GARCH multivariados realizados entre cada ação e os fatores. Comparamos nossa metodologia empíricamente com a literatura padrão, otimizando um portfólio no universo de ações do S&P500.Forecasting second moments of asset returns is essential in portfolio selection. In a multivariate setting, the dimensionality of the problem and the precision of predictions are the main concerns. We propose a new methodology for forecasting covariance matrices joining two extant approaches in the literature: intraday data to enhance predictive ability and factors to reduce the dimensionality. We assume a multivariate realized GARCH model for the factors and a set of multivariate realized GARCH between each stock and the factors. We compare our methodology empirically with the standard literature by optimizing a portfolio on the S&P500 stocks universe.engFinancial volatilityRealized GARCHHigh frequency dataMultivariate modelingCorrelation matrixFactorsVolatilidadeGARCH RealizadoDados em alta frequênciaModelos multivariadosMatriz de correlaçãoFatoresEconomiaVolatilidade (Finanças)Investimentos - AdministraçãoMercado financeiroAções (Finanças) - Preços - PrevisãoAnálise de séries temporaisRealized multivariate GARCH with factorsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVORIGINALRealized Multivariate GARCH with Factors.pdfRealized Multivariate GARCH with Factors.pdfPDFapplication/pdf672599https://repositorio.fgv.br/bitstreams/4a106fc0-7366-4ec4-8ef9-5b4301479879/download907f628347f34daae03ca6909d316e85MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-84707https://repositorio.fgv.br/bitstreams/a7a9cd67-aac8-435b-9efd-67d096d6ff3d/downloaddfb340242cced38a6cca06c627998fa1MD52TEXTRealized Multivariate GARCH with Factors.pdf.txtRealized Multivariate GARCH with Factors.pdf.txtExtracted texttext/plain48943https://repositorio.fgv.br/bitstreams/b96f0a8a-44d7-404a-bf82-30255d8342d2/download15f1edbf4c87001e0af987ddcc43488aMD55THUMBNAILRealized Multivariate GARCH with Factors.pdf.jpgRealized Multivariate GARCH with Factors.pdf.jpgGenerated Thumbnailimage/jpeg2497https://repositorio.fgv.br/bitstreams/86f2d4ff-f564-40e5-a812-f88c04519ae3/download97c8386d8fab48fcca9dd7400788860cMD5610438/330792023-11-25 02:04:27.138open.accessoai:repositorio.fgv.br:10438/33079https://repositorio.fgv.brRepositório 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dc.title.eng.fl_str_mv Realized multivariate GARCH with factors
title Realized multivariate GARCH with factors
spellingShingle Realized multivariate GARCH with factors
Coelho, Murilo Getlinger
Financial volatility
Realized GARCH
High frequency data
Multivariate modeling
Correlation matrix
Factors
Volatilidade
GARCH Realizado
Dados em alta frequência
Modelos multivariados
Matriz de correlação
Fatores
Economia
Volatilidade (Finanças)
Investimentos - Administração
Mercado financeiro
Ações (Finanças) - Preços - Previsão
Análise de séries temporais
title_short Realized multivariate GARCH with factors
title_full Realized multivariate GARCH with factors
title_fullStr Realized multivariate GARCH with factors
title_full_unstemmed Realized multivariate GARCH with factors
title_sort Realized multivariate GARCH with factors
author Coelho, Murilo Getlinger
author_facet Coelho, Murilo Getlinger
author_role author
dc.contributor.unidadefgv.por.fl_str_mv Escolas::EESP
dc.contributor.member.none.fl_str_mv Lunde, Asger
Medeiros, Marcelo Cunha
dc.contributor.author.fl_str_mv Coelho, Murilo Getlinger
dc.contributor.advisor1.fl_str_mv Fernandes, Marcelo
contributor_str_mv Fernandes, Marcelo
dc.subject.eng.fl_str_mv Financial volatility
Realized GARCH
High frequency data
Multivariate modeling
Correlation matrix
Factors
topic Financial volatility
Realized GARCH
High frequency data
Multivariate modeling
Correlation matrix
Factors
Volatilidade
GARCH Realizado
Dados em alta frequência
Modelos multivariados
Matriz de correlação
Fatores
Economia
Volatilidade (Finanças)
Investimentos - Administração
Mercado financeiro
Ações (Finanças) - Preços - Previsão
Análise de séries temporais
dc.subject.por.fl_str_mv Volatilidade
GARCH Realizado
Dados em alta frequência
Modelos multivariados
Matriz de correlação
Fatores
dc.subject.area.por.fl_str_mv Economia
dc.subject.bibliodata.por.fl_str_mv Volatilidade (Finanças)
Investimentos - Administração
Mercado financeiro
Ações (Finanças) - Preços - Previsão
Análise de séries temporais
description A previsão dos segundos momentos dos retornos de ativos é essencial na seleção de portfólio. Em um ambiente multivariado, a dimensionalidade do problema e a precisão das previsões são as principais preocupações. Propomos uma nova metodologia para prever matrizes de covariância unindo duas abordagens existentes na literatura: dados intradiários para aumentar a acurácia preditiva e fatores para reduzir a dimensionalidade. Assumimos um modelo GARCH realizado multivariado para os fatores e um conjunto de modelos GARCH multivariados realizados entre cada ação e os fatores. Comparamos nossa metodologia empíricamente com a literatura padrão, otimizando um portfólio no universo de ações do S&P500.
publishDate 2022
dc.date.issued.fl_str_mv 2022-12-13
dc.date.accessioned.fl_str_mv 2023-01-09T15:52:59Z
dc.date.available.fl_str_mv 2023-01-09T15:52:59Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
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dc.identifier.uri.fl_str_mv https://hdl.handle.net/10438/33079
url https://hdl.handle.net/10438/33079
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.source.none.fl_str_mv reponame:Repositório Institucional do FGV (FGV Repositório Digital)
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instname_str Fundação Getulio Vargas (FGV)
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institution FGV
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collection Repositório Institucional do FGV (FGV Repositório Digital)
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