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"
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  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  and Craiu and Lemieux  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)
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Journal Article: Efficient Gibbs sampling for Markov switching GARCH models (2016)
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