Bayesian inference for the log-symmetric autoregressive conditional duration model

Detalhes bibliográficos
Autor(a) principal: LEÃO,JEREMIAS
Data de Publicação: 2021
Outros Autores: PAIXÃO,RAFAEL, SAULO,HELTON, LEAO,THEMIS
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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spelling 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
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