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Theory and inference for a Markov switching Garch model

Author

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  • BAUWENS, Luc
  • PREMINGER, Arie
  • ROMBOUTS, Jeroen VK

Abstract

We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switch in time from one GARCH process to another. The switching is governed by a hidden Markov chain. We provide sufficient conditions for geometric ergodicity and existene of moments of the process. Because of path dependence, maximum likelihood estimation is not feasible. By enlarging the parameter space to include the state variables, Bayesian estimation using a Gibbs sampling algorithm is feasible. We illustrate the model on SP500 daily returns.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • BAUWENS, Luc & PREMINGER, Arie & ROMBOUTS, Jeroen VK, 2010. "Theory and inference for a Markov switching Garch model," LIDAM Reprints CORE 2303, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:2303
    DOI: 10.1111/j.1368-423X.2009.00307.x
    Note: In : Econometrics Journal, 13(2), 218-244, 2010
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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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