Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series
Autor(a) principal: | |
---|---|
Data de Publicação: | 2012 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1590/S0101-74382012005000019 http://hdl.handle.net/11449/28299 |
Resumo: | In this work we compared the estimates of the parameters of ARCH models using a complete Bayesian method and an empirical Bayesian method in which we adopted a non-informative prior distribution and informative prior distribution, respectively. We also considered a reparameterization of those models in order to map the space of the parameters into real space. This procedure permits choosing prior normal distributions for the transformed parameters. The posterior summaries were obtained using Monte Carlo Markov chain methods (MCMC). The methodology was evaluated by considering the Telebras series from the Brazilian financial market. The results show that the two methods are able to adjust ARCH models with different numbers of parameters. The empirical Bayesian method provided a more parsimonious model to the data and better adjustment than the complete Bayesian method. |
id |
UNSP_a77bf598b23d7c3a82cd20df610f052f |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/28299 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
spelling |
Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time seriesARCH modelsBayesian approachMCMC methodsIn this work we compared the estimates of the parameters of ARCH models using a complete Bayesian method and an empirical Bayesian method in which we adopted a non-informative prior distribution and informative prior distribution, respectively. We also considered a reparameterization of those models in order to map the space of the parameters into real space. This procedure permits choosing prior normal distributions for the transformed parameters. The posterior summaries were obtained using Monte Carlo Markov chain methods (MCMC). The methodology was evaluated by considering the Telebras series from the Brazilian financial market. The results show that the two methods are able to adjust ARCH models with different numbers of parameters. The empirical Bayesian method provided a more parsimonious model to the data and better adjustment than the complete Bayesian method.Fundação para o Desenvolvimento da UNESP (FUNDUNESP)Universidade Estadual PaulistaUniversidade de São Paulo Instituto de Ciências Matematicas e de Computação Departamento de Matemática Aplicada e EstatísticaUniversidade Estadual PaulistaSociedade Brasileira de Pesquisa OperacionalUniversidade Estadual Paulista (Unesp)Universidade de São Paulo (USP)Oliveira, Sandra C. [UNESP]Andrade, Marinho G.2014-05-20T15:12:10Z2014-05-20T15:12:10Z2012-08-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article293-313application/pdfhttp://dx.doi.org/10.1590/S0101-74382012005000019Pesquisa Operacional. Sociedade Brasileira de Pesquisa Operacional, v. 32, n. 2, p. 293-313, 2012.0101-7438http://hdl.handle.net/11449/2829910.1590/S0101-74382012005000019S0101-74382012000200003S0101-74382012000200003.pdf12689454348708140000-0002-0968-0108SciELOreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPesquisa Operacional0,365info:eu-repo/semantics/openAccess2024-06-10T14:49:01Zoai:repositorio.unesp.br:11449/28299Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:05:51.591649Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series |
title |
Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series |
spellingShingle |
Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series Oliveira, Sandra C. [UNESP] ARCH models Bayesian approach MCMC methods |
title_short |
Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series |
title_full |
Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series |
title_fullStr |
Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series |
title_full_unstemmed |
Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series |
title_sort |
Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series |
author |
Oliveira, Sandra C. [UNESP] |
author_facet |
Oliveira, Sandra C. [UNESP] Andrade, Marinho G. |
author_role |
author |
author2 |
Andrade, Marinho G. |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade de São Paulo (USP) |
dc.contributor.author.fl_str_mv |
Oliveira, Sandra C. [UNESP] Andrade, Marinho G. |
dc.subject.por.fl_str_mv |
ARCH models Bayesian approach MCMC methods |
topic |
ARCH models Bayesian approach MCMC methods |
description |
In this work we compared the estimates of the parameters of ARCH models using a complete Bayesian method and an empirical Bayesian method in which we adopted a non-informative prior distribution and informative prior distribution, respectively. We also considered a reparameterization of those models in order to map the space of the parameters into real space. This procedure permits choosing prior normal distributions for the transformed parameters. The posterior summaries were obtained using Monte Carlo Markov chain methods (MCMC). The methodology was evaluated by considering the Telebras series from the Brazilian financial market. The results show that the two methods are able to adjust ARCH models with different numbers of parameters. The empirical Bayesian method provided a more parsimonious model to the data and better adjustment than the complete Bayesian method. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-08-01 2014-05-20T15:12:10Z 2014-05-20T15:12:10Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1590/S0101-74382012005000019 Pesquisa Operacional. Sociedade Brasileira de Pesquisa Operacional, v. 32, n. 2, p. 293-313, 2012. 0101-7438 http://hdl.handle.net/11449/28299 10.1590/S0101-74382012005000019 S0101-74382012000200003 S0101-74382012000200003.pdf 1268945434870814 0000-0002-0968-0108 |
url |
http://dx.doi.org/10.1590/S0101-74382012005000019 http://hdl.handle.net/11449/28299 |
identifier_str_mv |
Pesquisa Operacional. Sociedade Brasileira de Pesquisa Operacional, v. 32, n. 2, p. 293-313, 2012. 0101-7438 10.1590/S0101-74382012005000019 S0101-74382012000200003 S0101-74382012000200003.pdf 1268945434870814 0000-0002-0968-0108 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Pesquisa Operacional 0,365 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
293-313 application/pdf |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Pesquisa Operacional |
publisher.none.fl_str_mv |
Sociedade Brasileira de Pesquisa Operacional |
dc.source.none.fl_str_mv |
SciELO reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128460288163840 |