Stochastic models with heteroskedasticity: a Bayesian approach for Ibovespa returns - doi: 10.4025/actascitechnol.v35i2.13547
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | por eng |
Título da fonte: | Acta scientiarum. Technology (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/13547 |
Resumo: | Current research compares the Bayesian estimates obtained for the parameters of processes of ARCH family with normal and Student’s t distributions for the conditional distribution of the return series. A non-informative prior distribution was adopted and a reparameterization of models under analysis was taken into account to map parameters’ space into real space. The procedure adopts a normal prior distribution for the transformed parameters. The posterior summaries were obtained by Monte Carlo Markov Chain (MCMC) simulation methods. The methodology was evaluated by a series of Bovespa Index returns and the predictive ordinate criterion was employed to select the best adjustment model to the data. Results show that, as a rule, the proposed Bayesian approach provides satisfactory estimates and that the GARCH process with Student’s t distribution adjusted better to the data. |
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Stochastic models with heteroskedasticity: a Bayesian approach for Ibovespa returns - doi: 10.4025/actascitechnol.v35i2.13547ARCH familyBayesian analysisMCMC methodsfinancial returnsInferência em Processos EstocásticosCurrent research compares the Bayesian estimates obtained for the parameters of processes of ARCH family with normal and Student’s t distributions for the conditional distribution of the return series. A non-informative prior distribution was adopted and a reparameterization of models under analysis was taken into account to map parameters’ space into real space. The procedure adopts a normal prior distribution for the transformed parameters. The posterior summaries were obtained by Monte Carlo Markov Chain (MCMC) simulation methods. The methodology was evaluated by a series of Bovespa Index returns and the predictive ordinate criterion was employed to select the best adjustment model to the data. Results show that, as a rule, the proposed Bayesian approach provides satisfactory estimates and that the GARCH process with Student’s t distribution adjusted better to the data. Universidade Estadual De Maringá2012-12-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionprocessos ARCH; inferência Bayesiana; métodos MCMCapplication/pdfapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/1354710.4025/actascitechnol.v35i2.13547Acta Scientiarum. Technology; Vol 35 No 2 (2013); 339-347Acta Scientiarum. Technology; v. 35 n. 2 (2013); 339-3471806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMporenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/13547/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/13547/pdf_1Oliveira, Sandra Cristina deAndrade, Marinho Gomes deinfo:eu-repo/semantics/openAccess2024-05-17T13:03:24Zoai:periodicos.uem.br/ojs:article/13547Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2024-05-17T13:03:24Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Stochastic models with heteroskedasticity: a Bayesian approach for Ibovespa returns - doi: 10.4025/actascitechnol.v35i2.13547 |
title |
Stochastic models with heteroskedasticity: a Bayesian approach for Ibovespa returns - doi: 10.4025/actascitechnol.v35i2.13547 |
spellingShingle |
Stochastic models with heteroskedasticity: a Bayesian approach for Ibovespa returns - doi: 10.4025/actascitechnol.v35i2.13547 Oliveira, Sandra Cristina de ARCH family Bayesian analysis MCMC methods financial returns Inferência em Processos Estocásticos |
title_short |
Stochastic models with heteroskedasticity: a Bayesian approach for Ibovespa returns - doi: 10.4025/actascitechnol.v35i2.13547 |
title_full |
Stochastic models with heteroskedasticity: a Bayesian approach for Ibovespa returns - doi: 10.4025/actascitechnol.v35i2.13547 |
title_fullStr |
Stochastic models with heteroskedasticity: a Bayesian approach for Ibovespa returns - doi: 10.4025/actascitechnol.v35i2.13547 |
title_full_unstemmed |
Stochastic models with heteroskedasticity: a Bayesian approach for Ibovespa returns - doi: 10.4025/actascitechnol.v35i2.13547 |
title_sort |
Stochastic models with heteroskedasticity: a Bayesian approach for Ibovespa returns - doi: 10.4025/actascitechnol.v35i2.13547 |
author |
Oliveira, Sandra Cristina de |
author_facet |
Oliveira, Sandra Cristina de Andrade, Marinho Gomes de |
author_role |
author |
author2 |
Andrade, Marinho Gomes de |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Oliveira, Sandra Cristina de Andrade, Marinho Gomes de |
dc.subject.por.fl_str_mv |
ARCH family Bayesian analysis MCMC methods financial returns Inferência em Processos Estocásticos |
topic |
ARCH family Bayesian analysis MCMC methods financial returns Inferência em Processos Estocásticos |
description |
Current research compares the Bayesian estimates obtained for the parameters of processes of ARCH family with normal and Student’s t distributions for the conditional distribution of the return series. A non-informative prior distribution was adopted and a reparameterization of models under analysis was taken into account to map parameters’ space into real space. The procedure adopts a normal prior distribution for the transformed parameters. The posterior summaries were obtained by Monte Carlo Markov Chain (MCMC) simulation methods. The methodology was evaluated by a series of Bovespa Index returns and the predictive ordinate criterion was employed to select the best adjustment model to the data. Results show that, as a rule, the proposed Bayesian approach provides satisfactory estimates and that the GARCH process with Student’s t distribution adjusted better to the data. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-12-03 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion processos ARCH; inferência Bayesiana; métodos MCMC |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/13547 10.4025/actascitechnol.v35i2.13547 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/13547 |
identifier_str_mv |
10.4025/actascitechnol.v35i2.13547 |
dc.language.iso.fl_str_mv |
por eng |
language |
por eng |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/13547/pdf http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/13547/pdf_1 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Technology; Vol 35 No 2 (2013); 339-347 Acta Scientiarum. Technology; v. 35 n. 2 (2013); 339-347 1806-2563 1807-8664 reponame:Acta scientiarum. Technology (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta scientiarum. Technology (Online) |
collection |
Acta scientiarum. Technology (Online) |
repository.name.fl_str_mv |
Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
||actatech@uem.br |
_version_ |
1799315334362038272 |