Show simple item record

dc.contributor.authorLivingston, Jr, Glen
dc.contributor.authorNur, Darfiana
dc.identifier.citationLivingston, Jr, G. and Nur, D. 2022. Bayesian inference of multivariate-GARCH-BEKK models. Statistical Papers.

The main aim of this paper is to present a Bayesian analysis of Multivariate GARCH(l, m) (M-GARCH) models including estimation of the coefficient parameters as well as the model order, by combining a set of existing MCMC algorithms in the literature. The proposed algorithm focuses on the BEKK formulation of the multivariate GARCH model. The estimation procedure will be designed as a custom MCMC with embedded Reversible Jump MCMC (RJMCMC) and Delayed Rejection Metropolis-Hastings (DRMH) steps implemented using the statistical software R. The RJMCMC steps allow three variants of BEKK models (constant, diagonal and full) to be indexed and this index included as a parameter to be estimated. The proposed MCMC algorithms are validated using extensive simulation experiments followed by a case study using bivariate data derived from the daily share prices for BHP Group Limited, Rio Tinto Group, and Fortescue Metals Group Limited on the ASX over from September 2013 to December 2021.

dc.publisherSpringer Nature
dc.titleBayesian inference of multivariate-GARCH-BEKK models
dc.typeJournal Article
dcterms.source.titleStatistical Papers
curtin.departmentSchool of Elec Eng, Comp and Math Sci (EECMS)
curtin.accessStatusOpen access
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidNur, Darfiana [0000-0002-7690-1097]
curtin.contributor.scopusauthoridNur, Darfiana [8921799600]

Files in this item


This item appears in the following Collection(s)

Show simple item record
Except where otherwise noted, this item's license is described as