DOES MRS-GARCH OUTPERFORMS THE SINGLE GARCH-TYPE MODELS FOR FORECASTING?
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Publication Date: | 2019 |
Other Authors: | |
Language: | eng |
Source: | Revista de Desenvolvimento Econômico (Online) - RDE |
Download full: | https://revistas.unifacs.br/index.php/rde/article/view/5726 |
Summary: | This paper evaluated if the Markov switching regime GARCH models can outperforms the single GARCH-type models on forecasting. To achieve this objective, it was used the Brazilian stock market – Ibovespa – between 2012 and 2017 as study scope. The major difference of our work is the definition of the in-sample and out-of-sample pattern. This paper determined a 200 steps-ahead (10 months) forecast, with model re-estimation at each step-ahead, in order to find conclusive evidence and robust results of the model which has better predictive ability. The results showed that the single GARCH-type models show a slightly better performance for VaR at 5% and switching regime models had better performance at 1% and predictive accuracy considering the most of statistical criteria. Besides that, no model could be determined as benchmark by statistical criteria, which displays that there´s no way to determine a model that outperforms for forecasting on the Brazilian stock market. |
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Revista de Desenvolvimento Econômico (Online) - RDE |
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DOES MRS-GARCH OUTPERFORMS THE SINGLE GARCH-TYPE MODELS FOR FORECASTING?O MRS-GARCH SUPERA OS MODELOS DO TIPO GARCH PARA PREVISÃO?Forecasting; Value-at-Risk; Volatility; MRS-GARCH; Stock marketPrevisão; Value-at-risk; Volatilidade; MRS-GARCH; Mercado acionárioThis paper evaluated if the Markov switching regime GARCH models can outperforms the single GARCH-type models on forecasting. To achieve this objective, it was used the Brazilian stock market – Ibovespa – between 2012 and 2017 as study scope. The major difference of our work is the definition of the in-sample and out-of-sample pattern. This paper determined a 200 steps-ahead (10 months) forecast, with model re-estimation at each step-ahead, in order to find conclusive evidence and robust results of the model which has better predictive ability. The results showed that the single GARCH-type models show a slightly better performance for VaR at 5% and switching regime models had better performance at 1% and predictive accuracy considering the most of statistical criteria. Besides that, no model could be determined as benchmark by statistical criteria, which displays that there´s no way to determine a model that outperforms for forecasting on the Brazilian stock market.Este trabalho avaliou se os modelos GARCH do regime de Markov podem superar os modelos do tipo GARCH na previsão. Para atingir este objetivo, foi utilizado o mercado acionário brasileiro - Ibovespa - entre 2012 e 2017 como escopo do estudo. A principal diferença do nosso trabalho é a definição do padrão dentro e fora da amostra. Este trabalho determinou uma previsão de 200 passos à frente (10 meses), com reestimação do modelo a cada passo à frente, a fim de encontrar evidências conclusivas e resultados robustos do modelo que tem melhor capacidade preditiva. Os resultados mostraram que os modelos do tipo GARCH mostram um desempenho ligeiramente melhor para o VaR a 5% e os modelos de regime de Markov tiveram melhor desempenho a 1% e precisão preditiva considerando a maioria dos critérios estatísticos. Além disso, conclui-se que nenhum modelo poderia ser determinado como referência por critérios estatísticos, o que mostra que não há como determinar um modelo que supera a previsão no mercado acionário brasileiro.Associação Brasileira de Editores CientíficosPereira Conte, Bruno; Universidade Federal de Santa MariaCeretta, Paulo Sérgio; UFSM2019-05-18info:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.unifacs.br/index.php/rde/article/view/572610.21452/rde.v3i41.5726RDE - Revista de Desenvolvimento Econômico; Ano XX - V. 3 - N. 41 - Dezembro de 2018reponame:Revista de Desenvolvimento Econômico (Online) - RDEinstname:Universidade Salvador (UNIFACS)instacron:UNIFACSA partir del momento del envío se asume automática la cesión de los derechos autorales para la Revista, toda vez que haya sido aprobado e aceptado para publicación.Autores que publicam nesta revista concordam com os seguintes termos: Autores que publicam na RDE mantêm os direitos sobre a sua obra e concedem à revista o direito de primeira publicação, realizada sob a Licença Creative Commons Attribution que permite o compartilhamento do trabalho e assevera o reconhecimento da autoria e do veículo de publicação original, a RDE.info:eu-repo/semantics/openAccesseng2019-05-18T15:34:09Zoai:ojs.200.223.74.126:article/5726Revistahttps://revistas.unifacs.br/index.php/rdePRIhttps://revistas.unifacs.br/index.php/rde/oaijose.gilea@animaeducacao.com.br || unifacs@nexodoc.com.br2178-80221516-1684opendoar:2019-05-18T15:34:09Revista de Desenvolvimento Econômico (Online) - RDE - Universidade Salvador (UNIFACS)false |
dc.title.none.fl_str_mv |
DOES MRS-GARCH OUTPERFORMS THE SINGLE GARCH-TYPE MODELS FOR FORECASTING? O MRS-GARCH SUPERA OS MODELOS DO TIPO GARCH PARA PREVISÃO? |
title |
DOES MRS-GARCH OUTPERFORMS THE SINGLE GARCH-TYPE MODELS FOR FORECASTING? |
spellingShingle |
DOES MRS-GARCH OUTPERFORMS THE SINGLE GARCH-TYPE MODELS FOR FORECASTING? Pereira Conte, Bruno; Universidade Federal de Santa Maria Forecasting; Value-at-Risk; Volatility; MRS-GARCH; Stock market Previsão; Value-at-risk; Volatilidade; MRS-GARCH; Mercado acionário |
title_short |
DOES MRS-GARCH OUTPERFORMS THE SINGLE GARCH-TYPE MODELS FOR FORECASTING? |
title_full |
DOES MRS-GARCH OUTPERFORMS THE SINGLE GARCH-TYPE MODELS FOR FORECASTING? |
title_fullStr |
DOES MRS-GARCH OUTPERFORMS THE SINGLE GARCH-TYPE MODELS FOR FORECASTING? |
title_full_unstemmed |
DOES MRS-GARCH OUTPERFORMS THE SINGLE GARCH-TYPE MODELS FOR FORECASTING? |
title_sort |
DOES MRS-GARCH OUTPERFORMS THE SINGLE GARCH-TYPE MODELS FOR FORECASTING? |
author |
Pereira Conte, Bruno; Universidade Federal de Santa Maria |
author_facet |
Pereira Conte, Bruno; Universidade Federal de Santa Maria Ceretta, Paulo Sérgio; UFSM |
author_role |
author |
author2 |
Ceretta, Paulo Sérgio; UFSM |
author2_role |
author |
dc.contributor.none.fl_str_mv |
|
dc.contributor.author.fl_str_mv |
Pereira Conte, Bruno; Universidade Federal de Santa Maria Ceretta, Paulo Sérgio; UFSM |
dc.subject.por.fl_str_mv |
Forecasting; Value-at-Risk; Volatility; MRS-GARCH; Stock market Previsão; Value-at-risk; Volatilidade; MRS-GARCH; Mercado acionário |
topic |
Forecasting; Value-at-Risk; Volatility; MRS-GARCH; Stock market Previsão; Value-at-risk; Volatilidade; MRS-GARCH; Mercado acionário |
description |
This paper evaluated if the Markov switching regime GARCH models can outperforms the single GARCH-type models on forecasting. To achieve this objective, it was used the Brazilian stock market – Ibovespa – between 2012 and 2017 as study scope. The major difference of our work is the definition of the in-sample and out-of-sample pattern. This paper determined a 200 steps-ahead (10 months) forecast, with model re-estimation at each step-ahead, in order to find conclusive evidence and robust results of the model which has better predictive ability. The results showed that the single GARCH-type models show a slightly better performance for VaR at 5% and switching regime models had better performance at 1% and predictive accuracy considering the most of statistical criteria. Besides that, no model could be determined as benchmark by statistical criteria, which displays that there´s no way to determine a model that outperforms for forecasting on the Brazilian stock market. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-05-18 |
dc.type.none.fl_str_mv |
|
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://revistas.unifacs.br/index.php/rde/article/view/5726 10.21452/rde.v3i41.5726 |
url |
https://revistas.unifacs.br/index.php/rde/article/view/5726 |
identifier_str_mv |
10.21452/rde.v3i41.5726 |
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.publisher.none.fl_str_mv |
Associação Brasileira de Editores Científicos |
publisher.none.fl_str_mv |
Associação Brasileira de Editores Científicos |
dc.source.none.fl_str_mv |
RDE - Revista de Desenvolvimento Econômico; Ano XX - V. 3 - N. 41 - Dezembro de 2018 reponame:Revista de Desenvolvimento Econômico (Online) - RDE instname:Universidade Salvador (UNIFACS) instacron:UNIFACS |
instname_str |
Universidade Salvador (UNIFACS) |
instacron_str |
UNIFACS |
institution |
UNIFACS |
reponame_str |
Revista de Desenvolvimento Econômico (Online) - RDE |
collection |
Revista de Desenvolvimento Econômico (Online) - RDE |
repository.name.fl_str_mv |
Revista de Desenvolvimento Econômico (Online) - RDE - Universidade Salvador (UNIFACS) |
repository.mail.fl_str_mv |
jose.gilea@animaeducacao.com.br || unifacs@nexodoc.com.br |
_version_ |
1834646030159708160 |