Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network

An appropriate calibration and forecasting of volatility and market risk are some of the main challenges faced by companies that have to manage the uncertainty inherent to their investments or funding opera- tions such as banks, pension funds or insurance companies. This has become even more evident...

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Autores: Ramos Pérez, Eduardo, Alonso González, Pablo Jesús|||0000-0002-4999-0151, Núñez Velázquez, José Javier|||0000-0002-7084-5629
Tipo de documento: artigo
Data de publicação:2019
País:España
Recursos:Universidad de Alcalá (UAH)
Repositório:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglês
OAI Identifier:oai:ebuah.uah.es:10017/59169
Acesso em linha:http://hdl.handle.net/10017/59169
https://dx.doi.org/10.1016/j.eswa.2019.03.046
Access Level:Acceso aberto
Palavra-chave:Machine learning
Stacking algorithms
Risk assessment
Volatility forecasting
Hybrid models
Economía
Economics
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spelling Forecasting volatility with a stacked model based on a hybridized Artificial Neural NetworkRamos Pérez, EduardoAlonso González, Pablo Jesús|||0000-0002-4999-0151Núñez Velázquez, José Javier|||0000-0002-7084-5629Machine learningStacking algorithmsRisk assessmentVolatility forecastingHybrid modelsEconomíaEconomicsAn appropriate calibration and forecasting of volatility and market risk are some of the main challenges faced by companies that have to manage the uncertainty inherent to their investments or funding opera- tions such as banks, pension funds or insurance companies. This has become even more evident after the 2007-2008 Financial Crisis, when the forecasting models assessing the market risk and volatility failed. Since then, a significant number of theoretical developments and methodologies have appeared to im- prove the accuracy of the volatility forecasts and market risk assessments. Following this line of thinking, this paper introduces a model based on using a set of Machine Learning techniques, such as Gradient Descent Boosting, Random Forest, Support Vector Machine and Artificial Neural Network, where those al- gorithms are stacked to predict S&P500 volatility. The results suggest that our construction outperforms other habitual models on the ability to forecast the level of volatility, leading to a more accurate assess- ment of the market risk20192019-03-27journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/59169https://dx.doi.org/10.1016/j.eswa.2019.03.046reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/591692026-06-18T11:13:07Z
dc.title.none.fl_str_mv Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network
title Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network
spellingShingle Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network
Ramos Pérez, Eduardo
Machine learning
Stacking algorithms
Risk assessment
Volatility forecasting
Hybrid models
Economía
Economics
title_short Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network
title_full Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network
title_fullStr Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network
title_full_unstemmed Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network
title_sort Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network
dc.creator.none.fl_str_mv Ramos Pérez, Eduardo
Alonso González, Pablo Jesús|||0000-0002-4999-0151
Núñez Velázquez, José Javier|||0000-0002-7084-5629
author Ramos Pérez, Eduardo
author_facet Ramos Pérez, Eduardo
Alonso González, Pablo Jesús|||0000-0002-4999-0151
Núñez Velázquez, José Javier|||0000-0002-7084-5629
author_role author
author2 Alonso González, Pablo Jesús|||0000-0002-4999-0151
Núñez Velázquez, José Javier|||0000-0002-7084-5629
author2_role author
author
dc.subject.none.fl_str_mv Machine learning
Stacking algorithms
Risk assessment
Volatility forecasting
Hybrid models
Economía
Economics
topic Machine learning
Stacking algorithms
Risk assessment
Volatility forecasting
Hybrid models
Economía
Economics
description An appropriate calibration and forecasting of volatility and market risk are some of the main challenges faced by companies that have to manage the uncertainty inherent to their investments or funding opera- tions such as banks, pension funds or insurance companies. This has become even more evident after the 2007-2008 Financial Crisis, when the forecasting models assessing the market risk and volatility failed. Since then, a significant number of theoretical developments and methodologies have appeared to im- prove the accuracy of the volatility forecasts and market risk assessments. Following this line of thinking, this paper introduces a model based on using a set of Machine Learning techniques, such as Gradient Descent Boosting, Random Forest, Support Vector Machine and Artificial Neural Network, where those al- gorithms are stacked to predict S&P500 volatility. The results suggest that our construction outperforms other habitual models on the ability to forecast the level of volatility, leading to a more accurate assess- ment of the market risk
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-03-27
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10017/59169
https://dx.doi.org/10.1016/j.eswa.2019.03.046
url http://hdl.handle.net/10017/59169
https://dx.doi.org/10.1016/j.eswa.2019.03.046
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:e_Buah Biblioteca Digital Universidad de Alcalá
instname:Universidad de Alcalá (UAH)
instname_str Universidad de Alcalá (UAH)
reponame_str e_Buah Biblioteca Digital Universidad de Alcalá
collection e_Buah Biblioteca Digital Universidad de Alcalá
repository.name.fl_str_mv
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