Forecasting daily volatility using high frequency financial data
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Institucional do FGV (FGV Repositório Digital) |
Texto Completo: | http://hdl.handle.net/10438/11994 |
Resumo: | Aiming at empirical findings, this work focuses on applying the HEAVY model for daily volatility with financial data from the Brazilian market. Quite similar to GARCH, this model seeks to harness high frequency data in order to achieve its objectives. Four variations of it were then implemented and their fit compared to GARCH equivalents, using metrics present in the literature. Results suggest that, in such a market, HEAVY does seem to specify daily volatility better, but not necessarily produces better predictions for it, what is, normally, the ultimate goal. The dataset used in this work consists of intraday trades of U.S. Dollar and Ibovespa future contracts from BM&FBovespa. |
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Alves, Thiago WinklerEscolas::EESPMarques, Alessandro MartimTakada, Hellinton HatsuoRuilova Terán, Juan Carlos2014-09-04T13:51:17Z2014-09-04T13:51:17Z2014-08-06ALVES, Thiago Winkler. Forecasting daily volatility using high frequency financial data. Dissertação (Mestrado Profissional em Finanças e Economia) - FGV - Fundação Getúlio Vargas, São Paulo, 2014.http://hdl.handle.net/10438/11994Aiming at empirical findings, this work focuses on applying the HEAVY model for daily volatility with financial data from the Brazilian market. Quite similar to GARCH, this model seeks to harness high frequency data in order to achieve its objectives. Four variations of it were then implemented and their fit compared to GARCH equivalents, using metrics present in the literature. Results suggest that, in such a market, HEAVY does seem to specify daily volatility better, but not necessarily produces better predictions for it, what is, normally, the ultimate goal. The dataset used in this work consists of intraday trades of U.S. Dollar and Ibovespa future contracts from BM&FBovespa.Objetivando resultados empíricos, este trabalho tem foco na eaplicação do modelo HEAVY para volatilidade diária com dados financeiros do mercado Brasileiro. Muito similar ao GARCH, este modelo busca explorar dados em alta frequência para atingir seus objetivos. Quatro variações dele foram então implementadas e seus ajustes comparadados a equivalentes GARCH, utilizando métricas presentes na literatura. Os resultados sugerem que, neste mercado, o HEAVY realmente parece especificar melhor a volatilidade diária, mas não necessariamente produz melhores previsões, o que, normalmente, é o objetivo final. A base de dados utilizada neste trabalho consite de negociações intradiárias de contratos futuros de dólares americanos e Ibovespa da BM&FBovespa.engFinancial engineeringVolatility forecastHigh frequency financial dataFutures marketEngenharia financeiraPrevisão de volatilidadeDados financeiros em alta frequênciaEconomiaMercado futuroAnálise de séries temporaisMercado financeiro - Modelos econométricosForecasting daily volatility using high frequency financial datainfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisreponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVinfo:eu-repo/semantics/openAccessORIGINALforecasting-daily-volatility.pdfforecasting-daily-volatility.pdfapplication/pdf885976https://repositorio.fgv.br/bitstreams/14429d28-31e3-4fc6-962f-9f04d36d1411/download30fb655def03c3f3e61bf930b3a3585bMD51LICENSElicense.txtlicense.txttext/plain; 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|
dc.title.eng.fl_str_mv |
Forecasting daily volatility using high frequency financial data |
title |
Forecasting daily volatility using high frequency financial data |
spellingShingle |
Forecasting daily volatility using high frequency financial data Alves, Thiago Winkler Financial engineering Volatility forecast High frequency financial data Futures market Engenharia financeira Previsão de volatilidade Dados financeiros em alta frequência Economia Mercado futuro Análise de séries temporais Mercado financeiro - Modelos econométricos |
title_short |
Forecasting daily volatility using high frequency financial data |
title_full |
Forecasting daily volatility using high frequency financial data |
title_fullStr |
Forecasting daily volatility using high frequency financial data |
title_full_unstemmed |
Forecasting daily volatility using high frequency financial data |
title_sort |
Forecasting daily volatility using high frequency financial data |
author |
Alves, Thiago Winkler |
author_facet |
Alves, Thiago Winkler |
author_role |
author |
dc.contributor.unidadefgv.por.fl_str_mv |
Escolas::EESP |
dc.contributor.member.none.fl_str_mv |
Marques, Alessandro Martim Takada, Hellinton Hatsuo |
dc.contributor.author.fl_str_mv |
Alves, Thiago Winkler |
dc.contributor.advisor1.fl_str_mv |
Ruilova Terán, Juan Carlos |
contributor_str_mv |
Ruilova Terán, Juan Carlos |
dc.subject.eng.fl_str_mv |
Financial engineering Volatility forecast High frequency financial data Futures market |
topic |
Financial engineering Volatility forecast High frequency financial data Futures market Engenharia financeira Previsão de volatilidade Dados financeiros em alta frequência Economia Mercado futuro Análise de séries temporais Mercado financeiro - Modelos econométricos |
dc.subject.por.fl_str_mv |
Engenharia financeira Previsão de volatilidade Dados financeiros em alta frequência |
dc.subject.area.por.fl_str_mv |
Economia |
dc.subject.bibliodata.por.fl_str_mv |
Mercado futuro Análise de séries temporais Mercado financeiro - Modelos econométricos |
description |
Aiming at empirical findings, this work focuses on applying the HEAVY model for daily volatility with financial data from the Brazilian market. Quite similar to GARCH, this model seeks to harness high frequency data in order to achieve its objectives. Four variations of it were then implemented and their fit compared to GARCH equivalents, using metrics present in the literature. Results suggest that, in such a market, HEAVY does seem to specify daily volatility better, but not necessarily produces better predictions for it, what is, normally, the ultimate goal. The dataset used in this work consists of intraday trades of U.S. Dollar and Ibovespa future contracts from BM&FBovespa. |
publishDate |
2014 |
dc.date.accessioned.fl_str_mv |
2014-09-04T13:51:17Z |
dc.date.available.fl_str_mv |
2014-09-04T13:51:17Z |
dc.date.issued.fl_str_mv |
2014-08-06 |
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.citation.fl_str_mv |
ALVES, Thiago Winkler. Forecasting daily volatility using high frequency financial data. Dissertação (Mestrado Profissional em Finanças e Economia) - FGV - Fundação Getúlio Vargas, São Paulo, 2014. |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10438/11994 |
identifier_str_mv |
ALVES, Thiago Winkler. Forecasting daily volatility using high frequency financial data. Dissertação (Mestrado Profissional em Finanças e Economia) - FGV - Fundação Getúlio Vargas, São Paulo, 2014. |
url |
http://hdl.handle.net/10438/11994 |
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 |
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