Journal of Economic Modeling Research

  In this study we compare a set of Markov Regime-Switching GARCH models in terms of their ability to forecast the Tehran stock market volatility at different time intervals. SW-GARCH models have been used to avoid the excessive persistence that usually found in GARCH models. In SW-GARCH models all parameters are allowed to switch between a low or high volatility regimes. Both Gaussian and fat-tailed conditional distributions are assumed for the residuals, and the degrees of freedom can also be state-dependent to capture possible time-varying kurtosis. Using stationary bootstrap and re-sampling, the forecasting performances of the competing models are evaluated by statistical loss functions. The empirical analysis demonstrates that SW-GARCH models outperform all standard GARCH models in forecasting volatility. Also, the SW-GARCH model with the t distribution for errors has the best performance in fitting a model and estimation.

This paper aims at estimating Geometric Brownian Motion (GBM) Model, based on two central parameters in this model (volatility and drift), and forecasting Henry Hub natural gas daily spot prices (07/01/1997-20/03/2012). Researches reveal that two mentioned parameters estimation can be satisfied with different approaches and in various time scales. Therefore, two approaches of backward looking and forward looking have been used in different time scales and sub-periods. Results show that the volatility and drift values are highly dependent on the time scale and backward results are lower than the forward ones. Moreover, along with increasing the number of random runs of the model although the fluctuating range decreases, the predicted line slope is very close to the actual line. Ultimately, the performance evaluation criteria yields that forward method, clearly in 2009, has the best performance. The sub-periods of 2001-2004 in backward and forward methods have the next best performances, respectively. These sub-periods can be used as a basis for calculating the central parameters of the model. In addition, the results suggest that relying on data used in the most recent period is not sufficiently accurate. Also, it is observed that sub-periods or time scales with higher volatility show better performance evaluation criteria, therefore they can be applied in price forecasting with GBM model.

This study examines the effect of Herding in different states (low, high and extreme volatility) in Tehran Stock Exchange during the years 2009-2013 using Chang et al (2000) and Balcilar et al (2013) models. In this survey herding are tested under 3 market regimes in selected industries: Cement, Chemical, Pharmaceutical and Investment.
The results don't show evidence of herding in 4 industries using static model (Chang et al, 2000). So dynamic model (Balcilar et al, 2013) was used  to analyze Herding under 3 regimes in which our results support the presence of  herding under 2 market regimes (high and extreme) . The results also demonstrate evidence of herding behavior under the high volatility regime for all of the selected industries. Herding under the extreme volatility regime is only found in investment and cement industries.

In this study by using Markov Regime Switching Heteroscedasticity Models (MRSH) in the form of state-space model the behavior of stock returns is examined. This approach endogenously permits the volatility to switch as the date and regime change and allows us to decompose the permanent and transitory component of stock returns. The period of the study is the fourth month of 2000 to the seventh month of 2013. The durations of the high-variance regimes for permanent components short-lived and revert to normal levels quickly and low variance regime for this components is more lasting, but durations of high-variance regime for transitory component is reverse. Also, in during periods of study low variance regime is dominant by a permanent component of stock returns but for the transitory component the high variance state is true captured.

The present article studies the interactive relationships between oil price volatility and industries stocks of basic metals, petroleum and chemical products by using Vector Auto Regressive (VAR) and Multivariate Generalized Autoregressive Conditional Heteroskedastisity (GARCH) models from March 2004 to March 2015 empirically . In this research, the VAR-GARCH model is proposed, which is developed by Ling and McAleer (2003). The model survives the return and volatility problems among the considered series and this is the VAR-GARCH advantage. The results show that there are Average effects between oil market and stocks market of basic metals and petroleum products, But this effects are not true for chemical industry market.  The volatility effects between world oil price and chemical and basic metals industry markets  is not existed, but between oil market volatility and petroleum products stock volatility, Significant negative relationship is existed. There for, the investors should reduce their portfolios basket dependences on oil price as much as possible.