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

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  • BAUWENS, Luc

    ()
    (Université catholique de Louvain, CORE, B-1348 Louvain-la-Neuve, Belgium)

  • DUFAYS, Arnaud

    ()
    (Université catholique de Louvain, CORE, B-1348 Louvain-la-Neuve, Belgium)

  • ROMBOUTS, Jeroen V.K.

    ()
    (HEC Montréal, CIRANO, CIRPEE and 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.

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Bibliographic Info

Paper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 2011013.

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Date of creation: 07 Dec 2011
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Handle: RePEc:cor:louvco:2011013

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Keywords: Bayesian inference; simulation; GARCH; Markov-switching model; change-point model; marginal likelihood; particle MCMC;

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