  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. The estimation
of the parameters by using the Bayesian method via Gibbs sampling algorithm is
studied. Finally we illustrate the efficiency of the model by simulation and
empirical analysis. We show that this model provides a much better forecast of
the volatility than the Markov switching GARCH model.
