Bayesian inference for the log-symmetric autoregressive conditional duration model
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Anais da Academia Brasileira de Ciências (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000700305 |
Resumo: | Abstract This paper adapts Hamiltonian Monte Carlo methods for application in log-symmetric autoregressive conditional duration models. These recent models are based on a class of log-symmetric distributions. In this class, it is possible to model both median and skewness of the duration time distribution. We use the Bayesian approach to estimate the model parameters of some log-symmetric autoregressive conditional duration models and evaluate their performance using a Monte Carlo simulation study. The usefulness of the estimation methodology is demonstrated by analyzing a high frequency financial data set from the German DAX of 2016. |
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Anais da Academia Brasileira de Ciências (Online) |
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Bayesian inference for the log-symmetric autoregressive conditional duration modelACD modelsBayesian inferencehigh frequency financial datalog-symmetric distributionsAbstract This paper adapts Hamiltonian Monte Carlo methods for application in log-symmetric autoregressive conditional duration models. These recent models are based on a class of log-symmetric distributions. In this class, it is possible to model both median and skewness of the duration time distribution. We use the Bayesian approach to estimate the model parameters of some log-symmetric autoregressive conditional duration models and evaluate their performance using a Monte Carlo simulation study. The usefulness of the estimation methodology is demonstrated by analyzing a high frequency financial data set from the German DAX of 2016.Academia Brasileira de Ciências2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000700305Anais da Academia Brasileira de Ciências v.93 n.4 2021reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765202120190301info:eu-repo/semantics/openAccessLEÃO,JEREMIASPAIXÃO,RAFAELSAULO,HELTONLEAO,THEMISeng2021-10-15T00:00:00Zoai:scielo:S0001-37652021000700305Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2021-10-15T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)false |
dc.title.none.fl_str_mv |
Bayesian inference for the log-symmetric autoregressive conditional duration model |
title |
Bayesian inference for the log-symmetric autoregressive conditional duration model |
spellingShingle |
Bayesian inference for the log-symmetric autoregressive conditional duration model LEÃO,JEREMIAS ACD models Bayesian inference high frequency financial data log-symmetric distributions |
title_short |
Bayesian inference for the log-symmetric autoregressive conditional duration model |
title_full |
Bayesian inference for the log-symmetric autoregressive conditional duration model |
title_fullStr |
Bayesian inference for the log-symmetric autoregressive conditional duration model |
title_full_unstemmed |
Bayesian inference for the log-symmetric autoregressive conditional duration model |
title_sort |
Bayesian inference for the log-symmetric autoregressive conditional duration model |
author |
LEÃO,JEREMIAS |
author_facet |
LEÃO,JEREMIAS PAIXÃO,RAFAEL SAULO,HELTON LEAO,THEMIS |
author_role |
author |
author2 |
PAIXÃO,RAFAEL SAULO,HELTON LEAO,THEMIS |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
LEÃO,JEREMIAS PAIXÃO,RAFAEL SAULO,HELTON LEAO,THEMIS |
dc.subject.por.fl_str_mv |
ACD models Bayesian inference high frequency financial data log-symmetric distributions |
topic |
ACD models Bayesian inference high frequency financial data log-symmetric distributions |
description |
Abstract This paper adapts Hamiltonian Monte Carlo methods for application in log-symmetric autoregressive conditional duration models. These recent models are based on a class of log-symmetric distributions. In this class, it is possible to model both median and skewness of the duration time distribution. We use the Bayesian approach to estimate the model parameters of some log-symmetric autoregressive conditional duration models and evaluate their performance using a Monte Carlo simulation study. The usefulness of the estimation methodology is demonstrated by analyzing a high frequency financial data set from the German DAX of 2016. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000700305 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000700305 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0001-3765202120190301 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Academia Brasileira de Ciências |
publisher.none.fl_str_mv |
Academia Brasileira de Ciências |
dc.source.none.fl_str_mv |
Anais da Academia Brasileira de Ciências v.93 n.4 2021 reponame:Anais da Academia Brasileira de Ciências (Online) instname:Academia Brasileira de Ciências (ABC) instacron:ABC |
instname_str |
Academia Brasileira de Ciências (ABC) |
instacron_str |
ABC |
institution |
ABC |
reponame_str |
Anais da Academia Brasileira de Ciências (Online) |
collection |
Anais da Academia Brasileira de Ciências (Online) |
repository.name.fl_str_mv |
Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC) |
repository.mail.fl_str_mv |
||aabc@abc.org.br |
_version_ |
1754302870871605248 |