Bayesian inference of smooth transition autoregressive (STAR)(k)–GARCH(l, m) models

Glen LivingstonJr, Darfiana Nur

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)2449-2482
Number of pages34
Issue number6
Early online date2018
Publication statusPublished - 1 Dec 2020


  • Generalised ARCH (GARCH)
  • Gibbs sampler algorithm
  • Metropolis–Hastings algorithm
  • Non-linear time series models
  • Regime switching volatility
  • Reversible jump MCMC algorithm


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