This paper introduces Markov-switching (MS) GARCH processes for capturing the skewness in the distribution of financial time series. The model class is motivated by the fact that empirical return distributions characterized by significant asymmetries, but the generic assumption of return distributions is Normal. The out of sample performance of symmetric and asymmetric MS GARCH models is compared in an application to Tehran exchange price index (Tepix). Finally, to put the Regime-switching models into perspective, we add to the list of competitors a popular model which may serve as a benchmark, i.e., the single–regime GARCH (1, 1). It turns out that asymmetric MS GARCH processes perform best overall.

In this paper we compare a set of different standard GARCH models with a group of Markov Regime-Switching GARCH (MRS-GARCH) in terms of their ability to forecast the petroleum futures markets volatility at horizons that range from one day to one month. To take into account the excessive persistence usually found in GARCH models that implies too smooth and too high volatility forecasts, MRS-GARCH models, where the parameters are allowed to switch between a low and a high volatility regime, are analyzed. Both gaussian and fat-tailed conditional distributions for the residuals are assumed, and the degrees of freedom can also be state-dependent to capture possible time-varying kurtosis. The forecasting performances of the competing models are evaluated with statistical loss functions. Under statistical losses, we use both tests of equal predictive ability of the Diebold-Mariano-type and test of superior predictive ability, such as White’s Reality Check and Hansen’s SPA test. The empirical analysis demonstrates that MRS-GARCH models do really outperform all standard GARCH models in forecasting volatility at shorter horizons according to a broad set of statistical loss functions. At longer horizons standard asymmetric GARCH models fare the best. All this tests reject the presence of a better model than the MRS-GARCH-t in this research.

Price volatility and volatility regime switching of Iranian important livestock markets are modeled using hay, sheep, calf, mutton, and beef monthly return series over the period of April 1992 to March 2014, using Markov-switching generalized autoregressive conditional hetroscedastisity models. The results suggest the existence of two volatility regimes in all studied markets and frequently switching from one regime to another all except for hay. Hay market is the most homogeneous market in terms of volatility regime switching while mutton market is the most variable one. According to results, although the high volatility regime lasts less than low volatility regime, persictency of high volatility regime (at least 5 month) in producer and retailer meat markets and frequently switching from one regime to another lead to uncertainty of investing in meat production. It also leads to unpredictability of market condition and variability of consumer’s welfare.

The housing market is one of the most important subsectors of capital markets that have the most backward and forward linkages with other sectors. Because of the high dependence of Iranian economy to oil revenues, oil price shocks can be affect the housing market. The aim of this study is to analyze the business cycle of the housing market, with emphasis on the impact of oil shocks on the return of housing. According to APM model, regardless of portfolio selected, several factors affect the return of housing assets that in this study, the risk and shocks of macroeconomic factors including money supply, private sector investment, housing facilities allocated by special Maskan Bank and oil export revenues, have been analyzed on the housing return by a Markov regime-switching GARCH model during the period 1982-2016. The results have shown that the return of housing in the Iranian economy has three high, moderate and low return regimes. So that the volatility of the housing return is different in each of the three regimes. Housing returns volatility at the low return regime is more than volatility of returns at the moderate and high return regimes. Therewith in the 35 years of the research period, housing market has been 13 years in the moderate return regime, 20 years in the low return regime and only 2 years in the high return regime. The results also showed that based on Dutch disease hypothesis oil shocks, liquidity and private investment have a significant positive impact on the return of housing but the housing facilities of Maskan Bank have a significant negative impact on the return of housing.

Considering the importance of implementing monetary policy in any economy, its impact on the main economic variables, including production, is very crucial. This paper examines and analyzes the effects of positive and negative monetary shocks on production in Iran using the MS-DSGE model during the period of 1979-2004. The results of this study indicate that positive and negative monetary policies in the recession periods, as well as boom periods, have asymmetric effects on the growth of domestic production. The effect of positive and negative monetary shocks on production is higher during the recession than during the boom. Generally, the monetary shocks are more effective during the boom than the recession. Accordingly, it is suggested that to achieve the economic goals policymakers should implement their policies during the economic recessions.

We have introduced an early warning system for volatility regimes regarding Tehran Stock Exchange using Markov Switching GARCH approach. We have examined whether Tehran Stock Market has calmed down or more specifically, whether the surge in volatility during 2007-2010 global financial crises still affects stock return volatility in Iran. Doing so, we have used a regime switching GARCH model. The data consist of 3067 daily observations of the closing value of the Tehran stock market from 29/09/1997 to 09/09/2010. The results indicate that during the crisis period, Tehran stock exchange was in the high-volatility regime. Smoothed probability plots show that the volatility in 2007-2009 was in high volatility regime but at 2009-2010, Volatility turned to low volatility regime. Also, we have introduced an early warning system for forecasting high volatility in Tehran Stock Exchange.