A GARCH tutorial with R
Main Author: | |
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Publication Date: | 2021 |
Other Authors: | , , |
Format: | Article |
Language: | eng |
Source: | Repositório Institucional da UFRGS |
Download full: | http://hdl.handle.net/10183/220139 |
Summary: | Context: modeling volatility is an advanced technique in financial econometrics, with several applications for academic research. Objective: in this tutorial paper, we will address the topic of volatility modeling in R. We will discuss the underlying logic of GARCH models, their representation and estimation process, along with a descriptive example of a real-world application of volatility modeling. Methods: we use a GARCH model to predict how much time it will take, after the latest crisis, for the Ibovespa index to reach its historical peak once again. The empirical data covers the period between years 2000 and 2020, including the 2009 financial crisis and the current 2020’s episode of the COVID-19 pandemic. Conclusion: we find that, according to our GARCH model, Ibovespa is more likely than not to reach its peak once again in one year and four months from June 2020. All data and R code used to produce this tutorial are freely available on the internet and all results can be easily replicated. |
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Perlin, Marcelo SchererMastella, MauroVancin, Daniel FranciscoRamos, Henrique Pinto2021-04-21T04:27:09Z20211415-6555http://hdl.handle.net/10183/220139001123234Context: modeling volatility is an advanced technique in financial econometrics, with several applications for academic research. Objective: in this tutorial paper, we will address the topic of volatility modeling in R. We will discuss the underlying logic of GARCH models, their representation and estimation process, along with a descriptive example of a real-world application of volatility modeling. Methods: we use a GARCH model to predict how much time it will take, after the latest crisis, for the Ibovespa index to reach its historical peak once again. The empirical data covers the period between years 2000 and 2020, including the 2009 financial crisis and the current 2020’s episode of the COVID-19 pandemic. Conclusion: we find that, according to our GARCH model, Ibovespa is more likely than not to reach its peak once again in one year and four months from June 2020. All data and R code used to produce this tutorial are freely available on the internet and all results can be easily replicated.application/pdfengRevista de administração contemporânea. Rio de Janeiro, RJ. Vol. 25, no. 1 (2021), p. 1-16VolatilidadeEconometriaMercado de açõesVolatilityGARCHTutorialA GARCH tutorial with RUm tutorial sobre Modelos Garch no R info:eu-repo/semantics/articleinfo:eu-repo/semantics/otherinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001123234.pdf.txt001123234.pdf.txtExtracted Texttext/plain62976http://www.lume.ufrgs.br/bitstream/10183/220139/2/001123234.pdf.txt8fcf47ed136706caa726a15a4ed97f45MD52ORIGINAL001123234.pdfTexto completo (inglês)application/pdf2795559http://www.lume.ufrgs.br/bitstream/10183/220139/1/001123234.pdf3d14043f15943e838f8de480b134b2ddMD5110183/2201392021-05-07 04:58:43.939446oai:www.lume.ufrgs.br:10183/220139Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2021-05-07T07:58:43Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
A GARCH tutorial with R |
dc.title.alternative.pt.fl_str_mv |
Um tutorial sobre Modelos Garch no R |
title |
A GARCH tutorial with R |
spellingShingle |
A GARCH tutorial with R Perlin, Marcelo Scherer Volatilidade Econometria Mercado de ações Volatility GARCH Tutorial |
title_short |
A GARCH tutorial with R |
title_full |
A GARCH tutorial with R |
title_fullStr |
A GARCH tutorial with R |
title_full_unstemmed |
A GARCH tutorial with R |
title_sort |
A GARCH tutorial with R |
author |
Perlin, Marcelo Scherer |
author_facet |
Perlin, Marcelo Scherer Mastella, Mauro Vancin, Daniel Francisco Ramos, Henrique Pinto |
author_role |
author |
author2 |
Mastella, Mauro Vancin, Daniel Francisco Ramos, Henrique Pinto |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Perlin, Marcelo Scherer Mastella, Mauro Vancin, Daniel Francisco Ramos, Henrique Pinto |
dc.subject.por.fl_str_mv |
Volatilidade Econometria Mercado de ações |
topic |
Volatilidade Econometria Mercado de ações Volatility GARCH Tutorial |
dc.subject.eng.fl_str_mv |
Volatility GARCH Tutorial |
description |
Context: modeling volatility is an advanced technique in financial econometrics, with several applications for academic research. Objective: in this tutorial paper, we will address the topic of volatility modeling in R. We will discuss the underlying logic of GARCH models, their representation and estimation process, along with a descriptive example of a real-world application of volatility modeling. Methods: we use a GARCH model to predict how much time it will take, after the latest crisis, for the Ibovespa index to reach its historical peak once again. The empirical data covers the period between years 2000 and 2020, including the 2009 financial crisis and the current 2020’s episode of the COVID-19 pandemic. Conclusion: we find that, according to our GARCH model, Ibovespa is more likely than not to reach its peak once again in one year and four months from June 2020. All data and R code used to produce this tutorial are freely available on the internet and all results can be easily replicated. |
publishDate |
2021 |
dc.date.accessioned.fl_str_mv |
2021-04-21T04:27:09Z |
dc.date.issued.fl_str_mv |
2021 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/other |
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article |
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http://hdl.handle.net/10183/220139 |
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1415-6555 |
dc.identifier.nrb.pt_BR.fl_str_mv |
001123234 |
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1415-6555 001123234 |
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http://hdl.handle.net/10183/220139 |
dc.language.iso.fl_str_mv |
eng |
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eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Revista de administração contemporânea. Rio de Janeiro, RJ. Vol. 25, no. 1 (2021), p. 1-16 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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