Market volatility : can machine learning methods enhance volatility forecasting?
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.14/42213 |
Resumo: | This dissertation aims to test whether the use of machine learning (ML) techniques can improve volatility forecasting accuracy. More specifically, if it can beat the best econometric model, the Heterogeneous Autoregressive model of Realized Volatility (HAR-RV). Using S&P 500 Index data from May-2007 to August-2022, the superiority of the HAR-RV was tested and attested against competing econometric models EWMA and GARCH(1,1). Next, the performance of the ML Artificial Neural Network algorithms Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are compared to the performance of the econometric models. Five different variable sets are tested for the ML models. It is found that while both ML models are able to beat the EWMA and GARCH(1,1) models by a significant margin, the HAR-RV model still outperforms LSTM and GRU. Moreover, an analysis is conduced on the models’ predictions on the period corresponding to the Covid-19 crisis. The results did not show any evidence suggesting that ML methods have a particular advantage at predicting during high volatility events. Finally, a plausible cause that could undermine the remarkable qualities of the ML methods in the aim of volatility forecasting is discussed. It is found that the rigorous set of conditions needed to be met for the proper setup of ML models are very difficult to be met using financial data, which hinders the aptitude of ML for this purpose. |
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Market volatility : can machine learning methods enhance volatility forecasting?Volatility forecastingHeterogeneous autoregressive modelMachine learningArtificial neural networksLong short-term memoryGated recurrent unitPrevisão de volatilidadeModelos heterogéneos autoregressivosRedes neurais artificiaisDomínio/Área Científica::Ciências Sociais::Economia e GestãoThis dissertation aims to test whether the use of machine learning (ML) techniques can improve volatility forecasting accuracy. More specifically, if it can beat the best econometric model, the Heterogeneous Autoregressive model of Realized Volatility (HAR-RV). Using S&P 500 Index data from May-2007 to August-2022, the superiority of the HAR-RV was tested and attested against competing econometric models EWMA and GARCH(1,1). Next, the performance of the ML Artificial Neural Network algorithms Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are compared to the performance of the econometric models. Five different variable sets are tested for the ML models. It is found that while both ML models are able to beat the EWMA and GARCH(1,1) models by a significant margin, the HAR-RV model still outperforms LSTM and GRU. Moreover, an analysis is conduced on the models’ predictions on the period corresponding to the Covid-19 crisis. The results did not show any evidence suggesting that ML methods have a particular advantage at predicting during high volatility events. Finally, a plausible cause that could undermine the remarkable qualities of the ML methods in the aim of volatility forecasting is discussed. It is found that the rigorous set of conditions needed to be met for the proper setup of ML models are very difficult to be met using financial data, which hinders the aptitude of ML for this purpose.Esta tese visa testar se o uso de técnicas de Machine Learning (ML) pode melhorar a precisão da previsão da volatilidade. Mais especificamente, se estes algoritmos conseguem superar o melhor modelo econométrico, o Heterogeneous Autoregressive model of Realized Volatility (HAR-RV). Usando dados do Índice S&P 500 de Maio-2007 a Agosto-2022, a superioridade do HAR-RV perante os modelos econométricos concorrentes EWMA e GARCH(1,1), foi testada e confirmada. Em seguida, o desempenho dos algoritmos ML de redes neurais artificiais de Long Short-Term Memory (LSTM) e Gated Recurrent Unit (GRU) são comparados com o desempenho dos modelos econométricos tradicionais. Cinco conjuntos diferentes de variáveis são testados para os modelos ML. Verifica-se que enquanto ambos os modelos ML são capazes de superar os modelos EWMA e GARCH(1,1) por uma margem significante, o modelo HARRV ainda tem um desempenho superior ao LSTM e ao GRU. É ainda feita uma análise das previsões dos modelos durante o período correspondente à crise do Covid-19. Os resultados não mostram qualquer evidência que sugira que os métodos ML têm uma particular vantagem durante eventos de alta volatilidade. Finalmente, é discutida uma possível causa que poderá debilitar as sofisticadas qualidades dos métodos ML para a finalidade de previsão de volatilidade. Verifica-se que o conjunto rigoroso de condições necessárias para a correcta configuração dos modelos ML é muito difícil de se cumprir utilizando series temporais de volatilidade de mercado, o que prejudica a aptidão dos modelos ML para esta finalidade.Faias, JoséVeritati - Repositório Institucional da Universidade Católica PortuguesaBatista, Afonso Maria Nabeto Valentim Xavier2023-10-04T00:30:48Z2023-05-052023-042023-05-05T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.14/42213TID:203299884enginfo: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:RCAAP2023-10-10T01:40:51Zoai:repositorio.ucp.pt:10400.14/42213Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:28:57.127350Repositório 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 |
Market volatility : can machine learning methods enhance volatility forecasting? |
title |
Market volatility : can machine learning methods enhance volatility forecasting? |
spellingShingle |
Market volatility : can machine learning methods enhance volatility forecasting? Batista, Afonso Maria Nabeto Valentim Xavier Volatility forecasting Heterogeneous autoregressive model Machine learning Artificial neural networks Long short-term memory Gated recurrent unit Previsão de volatilidade Modelos heterogéneos autoregressivos Redes neurais artificiais Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
title_short |
Market volatility : can machine learning methods enhance volatility forecasting? |
title_full |
Market volatility : can machine learning methods enhance volatility forecasting? |
title_fullStr |
Market volatility : can machine learning methods enhance volatility forecasting? |
title_full_unstemmed |
Market volatility : can machine learning methods enhance volatility forecasting? |
title_sort |
Market volatility : can machine learning methods enhance volatility forecasting? |
author |
Batista, Afonso Maria Nabeto Valentim Xavier |
author_facet |
Batista, Afonso Maria Nabeto Valentim Xavier |
author_role |
author |
dc.contributor.none.fl_str_mv |
Faias, José Veritati - Repositório Institucional da Universidade Católica Portuguesa |
dc.contributor.author.fl_str_mv |
Batista, Afonso Maria Nabeto Valentim Xavier |
dc.subject.por.fl_str_mv |
Volatility forecasting Heterogeneous autoregressive model Machine learning Artificial neural networks Long short-term memory Gated recurrent unit Previsão de volatilidade Modelos heterogéneos autoregressivos Redes neurais artificiais Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
topic |
Volatility forecasting Heterogeneous autoregressive model Machine learning Artificial neural networks Long short-term memory Gated recurrent unit Previsão de volatilidade Modelos heterogéneos autoregressivos Redes neurais artificiais Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
description |
This dissertation aims to test whether the use of machine learning (ML) techniques can improve volatility forecasting accuracy. More specifically, if it can beat the best econometric model, the Heterogeneous Autoregressive model of Realized Volatility (HAR-RV). Using S&P 500 Index data from May-2007 to August-2022, the superiority of the HAR-RV was tested and attested against competing econometric models EWMA and GARCH(1,1). Next, the performance of the ML Artificial Neural Network algorithms Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are compared to the performance of the econometric models. Five different variable sets are tested for the ML models. It is found that while both ML models are able to beat the EWMA and GARCH(1,1) models by a significant margin, the HAR-RV model still outperforms LSTM and GRU. Moreover, an analysis is conduced on the models’ predictions on the period corresponding to the Covid-19 crisis. The results did not show any evidence suggesting that ML methods have a particular advantage at predicting during high volatility events. Finally, a plausible cause that could undermine the remarkable qualities of the ML methods in the aim of volatility forecasting is discussed. It is found that the rigorous set of conditions needed to be met for the proper setup of ML models are very difficult to be met using financial data, which hinders the aptitude of ML for this purpose. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-10-04T00:30:48Z 2023-05-05 2023-04 2023-05-05T00:00:00Z |
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/10400.14/42213 TID:203299884 |
url |
http://hdl.handle.net/10400.14/42213 |
identifier_str_mv |
TID:203299884 |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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