On log-symmetric duration models applied to high frequency financial data
Helton Saulo and
Jeremias Leão ()
Additional contact information
Jeremias Leão: Department of Statistics, Federal University of Amazonas, Brazil
Economics Bulletin, 2017, vol. 37, issue 2, 1089-1097
Abstract:
This paper deals with a new generalization of autoregressive conditional duration (ACD) models. In special, we propose a new family of ACD models based on a class of log-symmetric distributions. In this new class, it is possible to model both median and skewness of the duration time distribution. We discuss maximum likelihood estimation of the model parameters. For illustrative purposes, we analyze a high frequency financial data set from the German DAX in 2016.
Keywords: Log-symmetric distributions; likelihood method; high frequency data; autoregressive conditional (search for similar items in EconPapers)
JEL-codes: C4 C5 (search for similar items in EconPapers)
Date: 2017-05-14
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://www.accessecon.com/Pubs/EB/2017/Volume37/EB-17-V37-I2-P95.pdf (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ebl:ecbull:eb-17-00030
Access Statistics for this article
More articles in Economics Bulletin from AccessEcon
Bibliographic data for series maintained by John P. Conley ().