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Maximum Likelihood Estimation of the Markov-Switching GARCH Model Based on a General Collapsing Procedure

Author

Listed:
  • Maciej Augustyniak

    (Université de Montréal
    Centre de recherches mathématiques)

  • Mathieu Boudreault

    (Centre de recherches mathématiques
    Université du Québec à Montréal)

  • Manuel Morales

    (Université de Montréal
    Centre de recherches mathématiques)

Abstract

The Markov-switching GARCH model allows for a GARCH structure with time-varying parameters. This flexibility is unfortunately undermined by a path dependence problem which complicates the parameter estimation process. This problem led to the development of computationally intensive estimation methods and to simpler techniques based on an approximation of the model, known as collapsing procedures. This article develops an original algorithm to conduct maximum likelihood inference in the Markov-switching GARCH model, generalizing and improving previously proposed collapsing approaches. A new relationship between particle filtering and collapsing procedures is established which reveals that this algorithm corresponds to a deterministic particle filter. Simulation and empirical studies show that the proposed method allows for a fast and accurate estimation of the model.

Suggested Citation

  • Maciej Augustyniak & Mathieu Boudreault & Manuel Morales, 2018. "Maximum Likelihood Estimation of the Markov-Switching GARCH Model Based on a General Collapsing Procedure," Methodology and Computing in Applied Probability, Springer, vol. 20(1), pages 165-188, March.
    • Handle: RePEc:spr:metcap:v:20:y:2018:i:1:d:10.1007_s11009-016-9541-4
    DOI: 10.1007/s11009-016-9541-4
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    Cited by:

    1. Maddalena Cavicchioli, 2021. "Statistical inference for mixture GARCH models with financial application," Computational Statistics, Springer, vol. 36(4), pages 2615-2642, December.

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