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Efficient Gibbs Sampling for Markov Switching GARCH Models

Monica Billio (), Roberto Casarin () and Ayokunle Osuntuyi ()

No 2012:35, Working Papers from Department of Economics, University of Venice "Ca' Foscari"

Abstract: We develop efficient simulation techniques for Bayesian inference on switching GARCH models. Our contribution to existing literature is manifold. First, we discuss different multi-move sampling techniques for Markov Switching (MS) state space models with particular attention to MS-GARCH models. Our multi-move sampling strategy is based on the Forward Filtering Backward Sampling (FFBS) applied to an approximation of MS-GARCH. Another important contribution is the use of multi-point samplers, such as the Multiple-Try Metropolis (MTM) and the Multiple trial Metropolize Independent Sampler, in combination with FFBS for the MS-GARCH process. In this sense we ex- tend to the MS state space models the work of So [2006] on efficient MTM sampler for continuous state space models. Finally, we suggest to further improve the sampler efficiency by introducing the antithetic sampling of Craiu and Meng [2005] and Craiu and Lemieux [2007] within the FFBS. Our simulation experiments on MS-GARCH model show that our multi-point and multi-move strategies allow the sampler to gain efficiency when compared with single-move Gibbs sampling.

Keywords: Bayesian inference; GARCH; Markov switching; Multiple-try Metropolis (search for similar items in EconPapers)
JEL-codes: C11 C15 C53 G17 (search for similar items in EconPapers)
Date: 2012
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Journal Article: Efficient Gibbs sampling for Markov switching GARCH models (2016) Downloads
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