Volatility forecasting with garch models and recurrent neural networks
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/154317 |
Resumo: | The three main ways to estimate future volatilities include the implied volatility of option prices, time-series volatility models, and neural network models. This project investigates whether there are economically meaningful differences between those approaches. Seminal time-series models like the GARCH, as well as recurrent neural network models like the LSTM are investigated to forecast volatilities. An eventual informational advantage over the market’s expectation of future volatility in the form of implied volatility is sought after. Through trading strategies involving options, as well as investment vehicles that emulate the VIX, it is attempted to trade volatility in a profitable way. |
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Volatility forecasting with garch models and recurrent neural networksVolatility forecastingImplied volatilityRealized volatilityVolatility risk premiumMachine learningNeural networksGarchLstmGruVixDomínio/Área Científica::Ciências Sociais::Economia e GestãoThe three main ways to estimate future volatilities include the implied volatility of option prices, time-series volatility models, and neural network models. This project investigates whether there are economically meaningful differences between those approaches. Seminal time-series models like the GARCH, as well as recurrent neural network models like the LSTM are investigated to forecast volatilities. An eventual informational advantage over the market’s expectation of future volatility in the form of implied volatility is sought after. Through trading strategies involving options, as well as investment vehicles that emulate the VIX, it is attempted to trade volatility in a profitable way.Hirschey, Nicholas H.RUNFerrari, Enrique Fabio2023-06-23T14:05:52Z2023-01-102022-12-162023-01-10T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/154317TID:203311639enginfo: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:RCAAP2024-03-11T05:36:47Zoai:run.unl.pt:10362/154317Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:55:35.027521Repositório 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 |
Volatility forecasting with garch models and recurrent neural networks |
title |
Volatility forecasting with garch models and recurrent neural networks |
spellingShingle |
Volatility forecasting with garch models and recurrent neural networks Ferrari, Enrique Fabio Volatility forecasting Implied volatility Realized volatility Volatility risk premium Machine learning Neural networks Garch Lstm Gru Vix Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
title_short |
Volatility forecasting with garch models and recurrent neural networks |
title_full |
Volatility forecasting with garch models and recurrent neural networks |
title_fullStr |
Volatility forecasting with garch models and recurrent neural networks |
title_full_unstemmed |
Volatility forecasting with garch models and recurrent neural networks |
title_sort |
Volatility forecasting with garch models and recurrent neural networks |
author |
Ferrari, Enrique Fabio |
author_facet |
Ferrari, Enrique Fabio |
author_role |
author |
dc.contributor.none.fl_str_mv |
Hirschey, Nicholas H. RUN |
dc.contributor.author.fl_str_mv |
Ferrari, Enrique Fabio |
dc.subject.por.fl_str_mv |
Volatility forecasting Implied volatility Realized volatility Volatility risk premium Machine learning Neural networks Garch Lstm Gru Vix Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
topic |
Volatility forecasting Implied volatility Realized volatility Volatility risk premium Machine learning Neural networks Garch Lstm Gru Vix Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
description |
The three main ways to estimate future volatilities include the implied volatility of option prices, time-series volatility models, and neural network models. This project investigates whether there are economically meaningful differences between those approaches. Seminal time-series models like the GARCH, as well as recurrent neural network models like the LSTM are investigated to forecast volatilities. An eventual informational advantage over the market’s expectation of future volatility in the form of implied volatility is sought after. Through trading strategies involving options, as well as investment vehicles that emulate the VIX, it is attempted to trade volatility in a profitable way. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-12-16 2023-06-23T14:05:52Z 2023-01-10 2023-01-10T00:00:00Z |
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/10362/154317 TID:203311639 |
url |
http://hdl.handle.net/10362/154317 |
identifier_str_mv |
TID:203311639 |
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 |
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1799138142856413184 |