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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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  This paper investigates the relationship between real exchange rate uncertainty and stock price index in Tehran stock exchange for the period of 1995-2009 by using monthly data and applying Bivariate Generalized Autoregressive Conditional Heteroskedasticity model (Bivariate GARCH). The results show that there is a negative and significant relationship between real exchange rate uncertainty and stock price index. However, the relationship between stock price uncertainty and real exchange rate is insignificant. Therefore, our results recommend that the policies which cause more volatility in the exchange market and also more volatility in the real exchange rate should be avoided to ensure the sustainable growth of the stock market and its price index.

  

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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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This paper presents an AHP and FLP model for the allocation of energy subsidies to different economic sectors. To do so, we defined a group of socio-economic criteria that may affected by the allocation of energy subsidies. These criteria are: economic growth, energy intensity, labor intensity, inflation, social cost of air pollutions and distribution of energy subsidy among socio-economic levels. According to calculated weights, we determined the priority of the above mentioned criteria. Also, according to the optimum overall rank of economic sectors, the commercial sector has the highest rank followed by industrial, agricultural and household and transportation sectors. After determining the final coefficients of AHP approach, we determined the allocation of energy subsidies using linier programming approach. We also considerd the change in technology and consumption patterns of household and transportation sectors. Results show that the share of energy subsidies allocated to commercial and transportation sectors should increase to 30.4 and 28.6 percent respectively.

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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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One of the empirical relationships that have been used to study the dynamics of labor market is the Beveridge curve -the scatter plot of unemployment rates versus vacancy rates- that is used to summarize the state of that market. The starting point for deriving the Beveridge curve is a matching function between unemployed workers and vacant jobs. In this research, provincial data are combined to estimate the Beveridge curve and the matching function of Iran. The matching function is estimated using provincial data for the period 1993-2008 and for estimating the Beveridge curve, provincial data for period 2005-2008 are used. The outcomes imply that the number of unemployed workers and vacant jobs has a positive and significant relationship with successful job matches. The elasticity of matching function for the unemployed and job vacancies equals 0.24 and 0.79, respectively. The Beveridge curve depicts a convex and negative relationship between unemployment rate and vacancy rate, although the relationship is not strong.

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Abstract

 Forecasting of crude oil price plays a crucial role in optimization of production, marketing and market strategies. Furthermore, it plays a significant role in government’s policies, because the government sets and implements its policies not only according to the current situation but also according to short run and long run predictions of important economic variables like oil price. The main purpose of this study is modeling and forecasting spot oil price of Iran by using GARCH model and A Gravitational Search Algorithm. Performed forecasts of this study are based in static and out-of-sample forecasting and each subseries data is divided in to two parts: data for estimation and data for forecasting. The forecast horizon is next leading period and its length is one month. In this study the selected models for forecasting spot oil of Iran are GARCH(2,1) and a Cobb Douglas function which is functional of prices of 5 days ago. Finally, the performances of these models are compared. For comparison of these models MSE, RMSE, MAE, and MAPE criteria are used and the results indicate that except in MAPE criterion, the mentioned criteria are smaller for GARCH model in comparison to GSA algorithm.

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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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The fluctuations in the oil price with uncertainty, as an exogenous variable, is the most important factor affecting the fluctuations in the GDP of the countries especially OPEC. This study examines the effect of oil price uncertainty on the Iran’s GDP growth using the seasonal data for the period 1988(1)-2011(4). The model used in this study is the asymmetric VARMA, MVGARCH-M and the estimated method is quasi maximum likelihood (QML). The results indicated that there is a negative and significant relationship between oil price and economic growth over the period. Furthermore, the results show that the conditional variance-covariance process underlying output growth and change in oil price exhibits non-diagonality and asymmetry.

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The main goal of this research is to calculate VaR index with parametric Markov-Switching GARCH approach for accepted companies in Tehran Stock Exchange and also selecting the optimal portfolio of their stocks. To calculate the index, data and information of weekly stock price of 10 representative firms during the period 2008-2014 has been used which account for 332 working weeks.
The results from estimation of VaR and determination of optimal stock portfolio in the non-linear programming framework showed that optimal portfolio of food-industry companies stock, in the context of VaR has higher returns and risk in the first regime (Boom period) compared to the second regime ( recession period). On the other hand, it has had lower weight in both stock portfolios that had lower average returns compared to the rest of the stocks and compared to the stocks which had lower VaR relative to other stocks that has higher weights.
The Kupiec and Lopez back testing using 10 future week data, showed that both of approaches is valid but the parametric approach has better rank. Therefore the optimal portfolios of stocks under parametric VaR will be accepted as final optimal portfolio.