Intraday volatility forecasting in high-frequency data using order book information

Detalhes bibliográficos
Autor(a) principal: Grübe, Maximilian
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/10362/73498
Resumo: This research conducts high-frequency intraday volatility forecasts on the Euro Stoxx 50 Future considering a multiplicative component GARCH framework, where the conditional volatility of high-frequency returns is decomposed into a daily, diurnal and stochastic intraday component. In contrast to extant research, in this work project a relatively long period of 423 trading days is covered corresponding to about 345.000 1-minute observations. To opt for a more practitioner-oriented approach we perform fixed window as well as rolling window forecasts. There is evidence that incorporating Limit Order Book information into the return series leads to superior forecasting results compared to the usage of simple trade returns. Nonetheless, the forecasting performance is time-varying and is often deteriorated by the seasonality of liquidity provision
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spelling Intraday volatility forecasting in high-frequency data using order book informationGarchVolatility forecastingHigh-frequency dataLimit order bookDomínio/Área Científica::Ciências Sociais::Economia e GestãoThis research conducts high-frequency intraday volatility forecasts on the Euro Stoxx 50 Future considering a multiplicative component GARCH framework, where the conditional volatility of high-frequency returns is decomposed into a daily, diurnal and stochastic intraday component. In contrast to extant research, in this work project a relatively long period of 423 trading days is covered corresponding to about 345.000 1-minute observations. To opt for a more practitioner-oriented approach we perform fixed window as well as rolling window forecasts. There is evidence that incorporating Limit Order Book information into the return series leads to superior forecasting results compared to the usage of simple trade returns. Nonetheless, the forecasting performance is time-varying and is often deteriorated by the seasonality of liquidity provisionRodrigues, Paulo Manuel MarquesRUNGrübe, Maximilian2019-06-24T14:42:36Z2019-01-142019-01-14T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/73498TID:202226158enginfo: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:RCAAP2024-03-11T04:34:01Zoai:run.unl.pt:10362/73498Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:35:20.352438Repositório 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 Intraday volatility forecasting in high-frequency data using order book information
title Intraday volatility forecasting in high-frequency data using order book information
spellingShingle Intraday volatility forecasting in high-frequency data using order book information
Grübe, Maximilian
Garch
Volatility forecasting
High-frequency data
Limit order book
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short Intraday volatility forecasting in high-frequency data using order book information
title_full Intraday volatility forecasting in high-frequency data using order book information
title_fullStr Intraday volatility forecasting in high-frequency data using order book information
title_full_unstemmed Intraday volatility forecasting in high-frequency data using order book information
title_sort Intraday volatility forecasting in high-frequency data using order book information
author Grübe, Maximilian
author_facet Grübe, Maximilian
author_role author
dc.contributor.none.fl_str_mv Rodrigues, Paulo Manuel Marques
RUN
dc.contributor.author.fl_str_mv Grübe, Maximilian
dc.subject.por.fl_str_mv Garch
Volatility forecasting
High-frequency data
Limit order book
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Garch
Volatility forecasting
High-frequency data
Limit order book
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description This research conducts high-frequency intraday volatility forecasts on the Euro Stoxx 50 Future considering a multiplicative component GARCH framework, where the conditional volatility of high-frequency returns is decomposed into a daily, diurnal and stochastic intraday component. In contrast to extant research, in this work project a relatively long period of 423 trading days is covered corresponding to about 345.000 1-minute observations. To opt for a more practitioner-oriented approach we perform fixed window as well as rolling window forecasts. There is evidence that incorporating Limit Order Book information into the return series leads to superior forecasting results compared to the usage of simple trade returns. Nonetheless, the forecasting performance is time-varying and is often deteriorated by the seasonality of liquidity provision
publishDate 2019
dc.date.none.fl_str_mv 2019-06-24T14:42:36Z
2019-01-14
2019-01-14T00:00:00Z
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/10362/73498
TID:202226158
url http://hdl.handle.net/10362/73498
identifier_str_mv TID:202226158
dc.language.iso.fl_str_mv eng
language eng
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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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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
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