Bayesian inference for the log-symmetric autoregressive conditional duration model

被引：2

作者：

Leao, Jeremias ^{[1
]}

Paixao, Rafael ^{[2
]}

Saulo, Helton ^{[3
]}

Leao, Themis ^{[1
]}

机构：

[1] Univ Fed Amazonas, Dept Estat, Campus Senador Arthur Virgilio Filho, BR-69080900 Manaus, Amazonas, Brazil

[2] Univ Sao Paulo, Dept Estat, Campus 1,Av Trab Sao Carlense 400, BR-400 Sao Carlos, SP, Brazil

[3] Univ Brasilia, Dept Estat, Campus Univ Darcy Ribeiro, BR-70910900 Brasilia, DF, Brazil

来源：

ANAIS DA ACADEMIA BRASILEIRA DE CIENCIAS | 2021年 / 93卷 / 04期

关键词：

ACD models; Bayesian inference; high frequency financial data; log-symmetric distributions; OUTLIER DETECTION; FAMILY; DISTRIBUTIONS; MANAGEMENT;

D O I：

10.1590/0001-3765202120190301

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：13

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