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

Author

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  • BAUWENS, Luc
  • DUFAYS, Arnaud
  • ROMBOUTS, Jeroen V.K.

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.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • BAUWENS, Luc & DUFAYS, Arnaud & ROMBOUTS, Jeroen V.K., 2014. "Marginal likelihood for Markov-switching and change-point GARCH models," LIDAM Reprints CORE 2533, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:2533
    Note: In : Journal of Econometrics, 178 (Part 3), 508-522, 2014
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    References listed on IDEAS

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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