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Marginal Likelihood for Markov-switching and Change-point Garch Models

  • Luc Luc

    ()

    (Université catholique de Louvain, CORE)

  • Arnaud Dufays

    ()

    (Université catholique de Louvain, CORE)

  • Jeroen V.K. Rombouts

    ()

    (Institute of Applied Economics at HEC Montréal, CIRANO, CIRPEE, and CORE)

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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File URL: ftp://ftp.econ.au.dk/creates/rp/11/rp11_41.pdf
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Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2011-41.

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Length: 34
Date of creation: 24 Nov 2011
Date of revision:
Handle: RePEc:aah:create:2011-41
Contact details of provider: Web page: http://www.econ.au.dk/afn/

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  1. Winfried Pohlmeier & Luc Bauwens & David Veredas, 2007. "High frequency financial econometrics. Recent developments," ULB Institutional Repository 2013/136223, ULB -- Universite Libre de Bruxelles.
  2. DUFAYS, Arnaud, 2012. "Infinite-state Markov-switching for dynamic volatility and correlation models," CORE Discussion Papers 2012043, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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