A Bayesian algorithm for a Markov Switching GARCH model
Applications of GARCH methods are now quite widespread in macroeconomic and financial time series. New formulations have been developed in order to address the statistical regularity observed in these time series such as assymetric nature and strong persistence of variances. This paper develops a ARMA-GARCH model with Markov switching conditional variances to simulataneously address the above two conditions. A Bayesian algorithm is developed for the estimation purpose and applied to two datasets
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