Theory and Inference for a Markov Switching GARCH Model
نویسندگان
چکیده
منابع مشابه
Theory and Inference for a Markov Switching Garch Model
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 existence of moments of the process. Because of path dependence, maximum likelihood estimation is not feasible. By enlarging the parameter...
متن کاملCore Discussion Paper 2007/55 Theory and Inference for a Markov Switching Garch Model
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 existence of moments of the process. Because of path dependence, maximum likelihood estimation is not feasible. By enlarging the parameter...
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Markov switching GARCH models have been developed in order to address the statistical regularity observed in financial time series such as strong persistence of conditional variance. However, Maximum Likelihood Estimation faces a implementation problem since the conditional variance depends on all the past history of state. This paper shows that this problem can be handled easily in Bayesian in...
متن کاملDeriving the autocovariances of powers of Markov-switching GARCH models, with applications to statistical inference
A procedure is proposed for computing the autocovariances and the ARMA representations of the squares, and higher-order powers, of Markov-switching GARCH models. It is shown that many interesting subclasses of the general model can be discriminated in view of their autocovariance structures. Explicit derivation of the autocovariances allows for parameter estimation in the general model, via a G...
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ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2007
ISSN: 1556-5068
DOI: 10.2139/ssrn.1011623