Using a dynamic artificial neural network for forecasting the volatility of a financial time series.

The ability to obtain accurate volatility forecasts is an important issue for the financial analyst. In this paper, we use the DAN2 model, a multilayer perceptronand an ARCH model to predict the monthly conditional variance of stock prices.The results show that DAN2 model is more accurate for predic...

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Autores:
Velásquez, Juan D.
Gutiérrez, Sarah
Franco, Carlos J.
Tipo de recurso:
Article of journal
Fecha de publicación:
2013
Institución:
Universidad de Medellín
Repositorio:
Repositorio UDEM
Idioma:
spa
OAI Identifier:
oai:repository.udem.edu.co:11407/962
Acceso en línea:
http://hdl.handle.net/11407/962
Palabra clave:
Volatility forecast
prediction
nonlinear models
heteroskedasticity
volatilidad (finanzas)
modelos no lineales
heterocedasticidad
Rights
License
http://creativecommons.org/licenses/by-nc-sa/4.0/
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spelling Velásquez, Juan D.Gutiérrez, SarahFranco, Carlos J.2014-10-22T23:26:11Z2014-10-22T23:26:11Z2013-06-301692-3324http://hdl.handle.net/11407/9622248-4094reponame:Repositorio Institucional Universidad de Medellínrepourl:https://repository.udem.edu.co/instname:Universidad de MedellínThe ability to obtain accurate volatility forecasts is an important issue for the financial analyst. In this paper, we use the DAN2 model, a multilayer perceptronand an ARCH model to predict the monthly conditional variance of stock prices.The results show that DAN2 model is more accurate for predicting in-sample andout-of-sample variance that the other considered models for the used data set. Thus, the value of this neural network as a predictive tool is demonstrated.Electrónicoapplication/pdfspaUniversidad de MedellínFacultad de IngenieríasMedellínhttp://revistas.udem.edu.co/index.php/ingenierias/article/view/637Revista Ingenierías Universidad de Medellínhttp://creativecommons.org/licenses/by-nc-sa/4.0/Attribution-NonCommercial-ShareAlike 4.0 Internationalhttp://purl.org/coar/access_right/c_abf2Revista Ingenierías Universidad de Medellín; Vol. 12, núm. 22 (2013)2248-40941692-3324Volatility forecastpredictionnonlinear modelsheteroskedasticityvolatilidad (finanzas)modelos no linealesheterocedasticidadUsing a dynamic artificial neural network for forecasting the volatility of a financial time series.Articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Artículo científicoinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85Comunidad Universidad de MedellínTHUMBNAILUsing a dynamic artificial neural network for forecasting the volatility of a financial time series..pdf.jpgUsing a dynamic artificial neural network for forecasting the volatility of a financial time series..pdf.jpgIM Thumbnailimage/jpeg6662http://repository.udem.edu.co/bitstream/11407/962/3/Using%20a%20dynamic%20artificial%20neural%20network%20for%20forecasting%20the%20volatility%20of%20a%20financial%20time%20series..pdf.jpg1c1ff823a710e99908a1ceb4956c98edMD53ORIGINALArticulo.htmltext/html574http://repository.udem.edu.co/bitstream/11407/962/1/Articulo.htmla89381039e84bda4f7e01e92e1569ba6MD51Using a dynamic artificial neural network for forecasting the volatility of a financial time series..pdfUsing a dynamic artificial neural network for forecasting the volatility of a financial time series..pdfTexto completoapplication/pdf287457http://repository.udem.edu.co/bitstream/11407/962/2/Using%20a%20dynamic%20artificial%20neural%20network%20for%20forecasting%20the%20volatility%20of%20a%20financial%20time%20series..pdf44431772a10df321e95d892338f1838bMD5211407/962oai:repository.udem.edu.co:11407/9622021-05-14 14:23:00.214Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co
dc.title.spa.fl_str_mv Using a dynamic artificial neural network for forecasting the volatility of a financial time series.
title Using a dynamic artificial neural network for forecasting the volatility of a financial time series.
spellingShingle Using a dynamic artificial neural network for forecasting the volatility of a financial time series.
Volatility forecast
prediction
nonlinear models
heteroskedasticity
volatilidad (finanzas)
modelos no lineales
heterocedasticidad
title_short Using a dynamic artificial neural network for forecasting the volatility of a financial time series.
title_full Using a dynamic artificial neural network for forecasting the volatility of a financial time series.
title_fullStr Using a dynamic artificial neural network for forecasting the volatility of a financial time series.
title_full_unstemmed Using a dynamic artificial neural network for forecasting the volatility of a financial time series.
title_sort Using a dynamic artificial neural network for forecasting the volatility of a financial time series.
dc.creator.fl_str_mv Velásquez, Juan D.
Gutiérrez, Sarah
Franco, Carlos J.
dc.contributor.author.none.fl_str_mv Velásquez, Juan D.
Gutiérrez, Sarah
Franco, Carlos J.
dc.subject.spa.fl_str_mv Volatility forecast
prediction
nonlinear models
heteroskedasticity
volatilidad (finanzas)
modelos no lineales
heterocedasticidad
topic Volatility forecast
prediction
nonlinear models
heteroskedasticity
volatilidad (finanzas)
modelos no lineales
heterocedasticidad
description The ability to obtain accurate volatility forecasts is an important issue for the financial analyst. In this paper, we use the DAN2 model, a multilayer perceptronand an ARCH model to predict the monthly conditional variance of stock prices.The results show that DAN2 model is more accurate for predicting in-sample andout-of-sample variance that the other considered models for the used data set. Thus, the value of this neural network as a predictive tool is demonstrated.
publishDate 2013
dc.date.created.none.fl_str_mv 2013-06-30
dc.date.accessioned.spa.fl_str_mv 2014-10-22T23:26:11Z
dc.date.available.spa.fl_str_mv 2014-10-22T23:26:11Z
dc.type.eng.fl_str_mv Article
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dc.identifier.issn.none.fl_str_mv 1692-3324
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dc.identifier.eissn.none.fl_str_mv 2248-4094
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional Universidad de Medellín
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dc.relation.ispartofjournal.spa.fl_str_mv Revista Ingenierías Universidad de Medellín
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dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.creativecommons.*.fl_str_mv Attribution-NonCommercial-ShareAlike 4.0 International
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Attribution-NonCommercial-ShareAlike 4.0 International
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dc.format.medium.spa.fl_str_mv Electrónico
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad de Medellín
dc.publisher.faculty.spa.fl_str_mv Facultad de Ingenierías
dc.publisher.place.spa.fl_str_mv Medellín
dc.source.spa.fl_str_mv Revista Ingenierías Universidad de Medellín; Vol. 12, núm. 22 (2013)
2248-4094
1692-3324
institution Universidad de Medellín
bitstream.url.fl_str_mv http://repository.udem.edu.co/bitstream/11407/962/3/Using%20a%20dynamic%20artificial%20neural%20network%20for%20forecasting%20the%20volatility%20of%20a%20financial%20time%20series..pdf.jpg
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