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Markov switching component GARCH model: Stability and forecasting

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  • N. Alemohammad
  • S. Rezakhah
  • S. H. Alizadeh

Abstract

This paper introduces an extension of the Markov switching GARCH model where the volatility in each state is a convex combination of two different GARCH components with time varying weights. This model has the dynamic behavior to capture the variants of shocks. The asymptotic behavior of the second moment is investigated and an appropriate upper bound for it is evaluated. Using the Bayesian method via Gibbs sampling algorithm, a dynamic method for the estimation of the parameters is proposed. Finally, we illustrate the efficiency of the model by simulation and also by considering two different set of empirical financial data. We show that this model provides much better forecasts of the volatility than the Markov switching GARCH model.

Suggested Citation

  • N. Alemohammad & S. Rezakhah & S. H. Alizadeh, 2016. "Markov switching component GARCH model: Stability and forecasting," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(15), pages 4332-4348, August.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:15:p:4332-4348
    DOI: 10.1080/03610926.2013.841934
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    Cited by:

    1. N. Alemohammad & S. Rezakhah & S. H. Alizadeh, 2020. "Markov switching asymmetric GARCH model: stability and forecasting," Statistical Papers, Springer, vol. 61(3), pages 1309-1333, June.

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