Markov switching GARCH models of currency turmoil in Southeast Asia

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...

Regime Switching Garch Models

We develop univariate regime-switching GARCH (RS-GARCH) models wherein the conditional variance switches in time from one GARCH process to another. The switching is governed by a time-varying probability, specified as a function of past information. We provide sufficient conditions for geometric ergodicity and existence of moments. Because of path dependence, maximum likelihood estimation is no...

Modeling carbon spot and futures price returns with GARCH and Markov switching GARCH models

This paper thoroughly investigates the price dynamics of carbon spot and futures returns for the first commitment period ranging from 2008 to 2012 with the aim to develop an adequate spot-returns-model. We apply a broad spectrum of various GARCH models including different distributions for model innovations. Both time series, spot and futures returns, exhibit asymmetric behavior in their varian...

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...