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

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Abstract

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.

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Acknowledgements

This paper arises from work in the PhD study of Dr Glen Livingston Jr who was funded by The Australian Postgraduate Award through the University of Newcastle, Australia.

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Authors and Affiliations

  1. School of Mathematical and Physical Sciences, The University of Newcastle, University Drive, Callaghan, NSW, 2308, Australia

    Glen Livingston Jr

  2. College of Science and Engineering, Flinders University, Sturt Road, Bedford Park, SA, 5042, Australia

    Darfiana Nur

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  1. Glen Livingston Jr
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  2. Darfiana Nur
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Correspondence to Darfiana Nur.

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Livingston, G., Nur, D. Bayesian inference of smooth transition autoregressive (STAR)(k)–GARCH(l, m) models. Stat Papers 61, 2449–2482 (2020). https://doi.org/10.1007/s00362-018-1056-3

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  • DOI: https://doi.org/10.1007/s00362-018-1056-3

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