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In this article the relationship between market return and volatility is examined by applying out- of- sample methodology and ARCH (M) class models in the Tehran Stock Exchange (TSE) and international stock exchanges. The results are inconsistent with portfolio theory implications in NASDAQ, ISE and TSE. However I found only negative relationship between unexpected volatility and monthly returns in most of international exchanges. I didn’t also find any significant relationship between forecasted volatility and monthly returns. The results contradict the asset pricing theories which explain a positive relationship between volatility and return. Although there are low coefficients of determination for all regressions, asymmetric volatility of return hypothesis explains this relationship in the sense that a decrease in stock price (negative return) increases the financial leverage of companies leading to more risky stocks and an eventually increasing volatility.

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  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.

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Price shocks lead to oil price volatility in world oil markets. In response to this volatility, economic growth may take different regime and behavior patterns in different situation. Investigating this multi behavior patterns can be useful for policymakers to reduce the effect of oil price volatility. In this study, an EGARCH model has developed using the seasonal data of OPEC oil basket nominal prices during 1367:Q1-1389:Q4. Markov switching models is also applied to investigate the multi behavior patterns of economic growth in response to oil price volatility in Iran. The results show that positive oil price shocks sharply lead to formation of oil price volatility, but, the negative price shocks will slightly reduce oil price volatility. Iranian economic growth is affected by this volatility under three different behavior regimes. If the economy switch to one of the regimes (low, medium, high economic growth), the probability of transition between these regimes and their duration is different. So, oil price volatility as a reason for low economic growth in Iran may cause the economy switch to its lower situation.

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Iran’s share of world exports has not been great in recent years and the development of non-oil exports such as exports of industrial goods in order to reduce the economy's dependence on oil revenues made necessary. The real exchange rate is one of the most important variables affecting exports. In this context, investigate the effect of the real exchange rate volatility on different variables such as the export is important. The main objective of this paper is to investigate the impact of real exchange rate volatility on exports of Iran Industrial goods over the period of 1968-2010. To that end, The real exchange rate volatility index has been estimated incorporating with EGARCH (0,1)  model than we using co-integration of  Saikkonen & Lutkepohl and FMOLS to investigate the impact of the real exchange rate volatility index, along with other variables of model exports of industrial goods have been evaluated.
The main empirical finding of this paper show that the real exchange rate volatility variables and export prices have negative and significant effects on exports of industrial goods and variables GDP’s world, GDP’s Iran and trade  of openness  have positive and significant effects on exports of industrial goods. The empirical findings of this paper, The beneficial implications for investors and Policy makers needs to recognize the exact effects of exchange rate volatility on exports of industrial goods are provided.

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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.