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This paper examines the calendar anomalies in daily return of the Tehran stock market. ARCH and GARCH models are employed to capture the wide range of different calendar anomalies exist in the literature. This study finds the evidence of strong Esfand and Mehr effects in the stock return. In addition, the results show that the stock market return has decreased with the lapses of time. After identifying and removing the calendar effects from daily return, BDS statistic is used to test the presence of any remaining non-linearity in the residuals before employing the GARCH models. The BDS test shows that there is a high probability of the dependency between residuals in spite of removing calendar anomalies. The results confirm that both the ARCH and GARCH models have considerable success in modeling dependencies. Finally, the importance of calendar effects in return forecasting is tested. The conclusion is that the inclusion of calendar effects improves the forecast accuracy. However, simple regression which includes calendar effects has better performance than the GARCH (1, 1) models.

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One of the key concepts in risk managing of financial portfolios is the probability based risk measurement method known as value at risk. During recent years, various methods have been introduced by researchers to compute this criterion. Because of their dissimilar assumptions and procedures, making the use of each of which creates different results. Therefore, this paper uses two main methods in order to measure the value at risk of foreign exchange portfolio. They comprise generalized autoregressive conditional heteroskedasticity model and Monte Carlo Simulation. Using failure rate back testing, the results of these methods are compared. The results of the evaluation demonstrate that the mentioned methods have different performances.

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Hyper inflation rates impose direct and indirect costs upon society. It has undesirable consequences that are caused by inflation uncertainty. In this regard, the following questions are raised: How do inflation rate and its uncertainty affect economic growth? Does the structural breakpoint affect relationship between inflation and growth rate? In this study the above questions are examined for the Iran's economy in period 1974-2007. For this purpose the regressive model is applied. In this model, growth rate of GDP depends on inflation rate, growth rate of the money supply, growth rate of the real gross fixed capital formation and inflation uncertainty. For the measuring inflation uncertainty Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used. Based on data analysis, structural break point occurs at inflation rate equal to 20 percent. Results show that the impact of inflation rate on economic growth is significantly negative but it minimizes at the rate of less than 20 percent and increases at the rate of more than 20 percent. Moreover, inflation uncertainty has significant and negative effect on growth.

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The long run economic growth is one of the main economic requirements of countries in order to attain comprehensive development and increase the social welfare. This research aims to examine the effect of output uncertainty on economic growth for Iran during 1965-2011. Output uncertainty, gross domestic product, inflation and population are variables under study. In this paper, first, output uncertainty is computed using a generalized auto-regressive conditional heteroscedasticity (GARCH) model and then the effect of output uncertainty on economic growth is estimated though co-integration test and vector auto-regression (VAR). The findings show that output uncertainty reduces the long run economic growth in Iran. This result is in accordance with Bernanke (1983) and Pindyck (1991) studies. They concluded that increase in output uncertainty leads to decrease in both investment and long run economic growth. The findings also indicate the negative and positive effects of inflation and population growth, respectively, on the long run economic growth in Iran. 

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This research examines the effect of monetary policies on unemployment under inflation uncertainty in Iran using the annual data during 1974-2011. The basic model is selected according to the simultaneous equilibrium of dynamic aggregate demand and supply. In addition, inflation uncertainty is calculated using the GARCH family models including ARCH, GARCH and EGARCH. The generated data from a novel model is considered as a proxy for inflation uncertainty, and Generalized Method of Moments (GMM) is used to estimate this model. The estimation results show that inflation uncertainty reduces the unemployment rate, i.e. the effect of monetary policies on unemployment is decreased under inflation uncertainty, and there is a significant and positive relationship between unemployment and inflation rates. Henceforth, an increase in inflation uncertainty leads to an increase in unemployment rate, which is in line with Friedman's theory in this field

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The real effective exchange rate and its uncertainty are among the most important macroeconomic variables that affect different economic sectors from various aspects. Since the changes in exchange rate have no identical impacts on all sectors of the economy and regarding considerable importance of industrial development on economic development, this study examines and evaluates the effects of real effective of exchange rate and its uncertainty on the value-added of industrial subsectors based on the two-digit codes ISIC-REV4 using Panel data and Engel-Granger methods during 1979-2014. The results show that the real effective exchange rate is of different effects on various subsectors of the industry while its uncertainty has no effect on sub-sectors’ value-added.  Consequently, there is no single exchange rate policy in industrial sector due to different foreign exchange requirements in its subsectors.

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Volatility and imperfect price transmission in food markets always impress the welfare of producers and consumers, especially in the developing countries. Therefore, the purpose of this study was to investigate the price relationship in vertical market levels (i.e. farm gate, wholesale and retail) of rice as a staple food for Iranians, using the Vector Error Correction Model (VECM) and the Generalized AutoRegressive Conditional Heteroskedastic (GARCH). The data used was based on monthly observations of prices in Kamfiroz Rice Market from April 1997 to March 2015. Results showed that the direction of Granger causality and partial price transmission were from farm gate to retail market as well as from wholesale to farm gate level and retail market to wholesale, such that, if wholesale prices increase by 1%, farm gate prices will increase about 0.37%. Also, if retail prices increase by 1%, then wholesale prices will increase by about 0.36%. In addition, if farm gate prices increase by 1%, then retail prices will decrease by about 0.08%. Results also implied that retail and wholesale price volatilities have positive spillover effects on the volatility of farm gate prices (i.e. 0.50 and 0.31, respectively). In addition, retail prices are more sensitive to wholesale prices and more volatile (i.e. 0.56) than the others. Finally, in order to increase the transparency of information and increase the efficiency of price transmission in Kamfiroz Rice Market, it was suggested that marketing cooperatives of this product be increased and supported more.