Marginal likelihood for Markov-switching and change-point GARCH models

Luc Bauwens, Arnaud Dufays and Jeroen Rombouts

No 2011013, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: GARCH volatility models with fixed parameters are too restrictive for long time series due to breaks in the volatility process. Flexible alternatives are Markov-switching GARCH and change-point GARCH models. They require estimation by MCMC methods due to the path dependence problem. An unsolved issue is the computation of their marginal likelihood, which is essential for determining the number of regimes or change-points. We solve the problem by using particle MCMC, a technique proposed by Andrieu, Doucet, and Holenstein (2010). We examine the performance of this new method on simulated data, and we illustrate its use on several return series.

Keywords: Bayesian inference; simulation; GARCH; Markov-switching model; change-point model; marginal likelihood; particle MCMC (search for similar items in EconPapers)
JEL-codes: C11 C15 C22 C58 (search for similar items in EconPapers)
Date: 2011-12-07
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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Related works:
Journal Article: Marginal likelihood for Markov-switching and change-point GARCH models (2014)
Working Paper: Marginal likelihood for Markov-switching and change-point GARCH models (2014)
Working Paper: Marginal Likelihood for Markov-switching and Change-point Garch Models (2011)
Working Paper: Marginal Likelihood for Markov-Switching and Change-Point Garch Models (2011)
Working Paper: Marginal Likelihood for Markov-Switching and Change-Point GARCH Models (2011)
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