Bayesian inference for the log-symmetric autoregressive conditional duration model

Bayesian inference for the log-symmetric autoregressive conditional duration model

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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Main Authors: LEÃO,JEREMIAS, PAIXÃO,RAFAEL, SAULO,HELTON, LEAO,THEMIS
Format: Digital revista
Language:English
Published: Academia Brasileira de Ciências 2021
Online Access:http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000700305
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spelling oai:scielo:S0001-376520210007003052021-10-15Bayesian inference for the log-symmetric autoregressive conditional duration modelLEÃO,JEREMIASPAIXÃO,RAFAELSAULO,HELTONLEAO,THEMIS ACD models Bayesian inference high frequency financial data log-symmetric distributions 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.info:eu-repo/semantics/openAccessAcademia Brasileira de CiênciasAnais da Academia Brasileira de Ciências v.93 n.4 20212021-01-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000700305en10.1590/0001-3765202120190301
institution SCIELO
collection OJS
country Brasil
countrycode BR
component Revista
access En linea
databasecode rev-scielo-br
tag revista
region America del Sur
libraryname SciELO
language English
format Digital
author LEÃO,JEREMIAS
PAIXÃO,RAFAEL
SAULO,HELTON
LEAO,THEMIS
spellingShingle LEÃO,JEREMIAS
PAIXÃO,RAFAEL
SAULO,HELTON
LEAO,THEMIS
Bayesian inference for the log-symmetric autoregressive conditional duration model
author_facet LEÃO,JEREMIAS
PAIXÃO,RAFAEL
SAULO,HELTON
LEAO,THEMIS
author_sort LEÃO,JEREMIAS
title Bayesian inference for the log-symmetric autoregressive conditional duration model
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
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.
publisher Academia Brasileira de Ciências
publishDate 2021
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000700305
work_keys_str_mv AT leaojeremias bayesianinferenceforthelogsymmetricautoregressiveconditionaldurationmodel
AT paixaorafael bayesianinferenceforthelogsymmetricautoregressiveconditionaldurationmodel
AT saulohelton bayesianinferenceforthelogsymmetricautoregressiveconditionaldurationmodel
AT leaothemis bayesianinferenceforthelogsymmetricautoregressiveconditionaldurationmodel
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