MODEL VOLATILITAS SAHAM LQ45 DENGAN PENDEKATAN MARKOV-SWITCHING GARCH

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...

Long memory with Markov-Switching GARCH

Modeling the Effective Factors on Economic Growth in Iran: Markov Switching GARCH Approach

Economic growth is one of the most important macroeconomic indicators in each country that its sustainability for a long time is one of the most basic and necessary conditions to achieve economic development.  The aim of this paper is modeling the effective factors of economic growth in Iran concerning the period 1973-2014 using a Markov switching GARCH model (MS-GARCH). The contribution of thi...

On the Stationarity of Markov-Switching GARCH Processes

GARCH models with Markov-switching regimes are often used for volatility analysis of …nancial time series. Such models imply less persistence in the conditional variance than the standard GARCH model, and potentially provide a signi…cant improvement in volatility forecast. Nevertheless, conditions for asymptotic wide-sense stationarity have been derived only for some degenerated models. In this...

A Bayesian MCMC Algorithm for Markov Switching GARCH models

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...