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dc.contributor.authorLivingston, G.
dc.contributor.authorNur, Darfiana
dc.date.accessioned2020-06-12T04:35:51Z
dc.date.available2020-06-12T04:35:51Z
dc.date.issued2018
dc.identifier.citationLivingston, G. and Nur, D. 2018. Bayesian inference of smooth transition autoregressive (STAR)(k)–GARCH(l, m) models. Statistical Papers.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/79608
dc.identifier.doi10.1007/s00362-018-1056-3
dc.description.abstract

© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The smooth transition autoregressive (STAR)(k)–GARCH(l, m) model is a non-linear time series model that is able to account for changes in both regime and volatility respectively. The model can be widely applied to analyse the dynamic behaviour of data exhibiting these two phenomenons in areas such as finance, hydrology and climate change. The main aim of this paper is to perform a Bayesian analysis of STAR(k)–GARCH(l, m) models. The estimation procedure will include estimation of the mean and variance coefficient parameters, the parameters of the transition function, as well as the model orders (k, l, m). To achieve this aim, the joint posterior distribution of the model orders, coefficient and implicit parameters in the logistic STAR(k)–GARCH(l, m) model is presented. The conditional posterior distributions are then derived, followed by the design of a posterior simulator using a combination of MCMC algorithms which includes Metropolis–Hastings, Gibbs Sampler and Reversible Jump MCMC algorithms. Following this are extensive simulation studies and a case study presenting the methodology.

dc.titleBayesian inference of smooth transition autoregressive (STAR)(k)–GARCH(l, m) models
dc.typeJournal Article
dcterms.source.issn0932-5026
dcterms.source.titleStatistical Papers
dc.date.updated2020-06-12T04:35:51Z
curtin.departmentSchool of Elec Eng, Comp and Math Sci (EECMS)
curtin.accessStatusFulltext not available
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidNur, Darfiana [0000-0002-7690-1097]


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