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

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Author Info

  • 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)

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 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

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: Bayesian inference; Simulation; GARCH; Markov-switching model; Changepoint model; Marginal likelihood; Particle MCMC;

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References

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Citations

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Cited by:
  1. ROMBOUTS, Jeroen V.K. & STENTOFT, Lars, 2009. "Bayesian option pricing using mixed normal heteroskedasticity models," CORE Discussion Papers, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) 2009013, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. Jin, Xin & Maheu, John M, 2014. "Modeling Covariance Breakdowns in Multivariate GARCH," MPRA Paper 55243, University Library of Munich, Germany.
  3. repec:luc:wpaper:14-07 is not listed on IDEAS
  4. Menelaos Karanasos & Alexandros Paraskevopoulos & Faek Menla Ali & Michail Karoglou & Stavroula Yfanti, 2014. "Modelling Returns and Volatilities During Financial Crises: a Time Varying Coefficient Approach," Papers 1403.7179, arXiv.org.
  5. Augustyniak, Maciej, 2014. "Maximum likelihood estimation of the Markov-switching GARCH model," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 76(C), pages 61-75.

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