IRANIAN JOURNAL OF APPLIED ECONOMICS
Year:2017 | Volume:7 | Issue:22 |Papers Count:6

Exchange rate as one of the key variables in the economy, has important effects on macroeconomic variables such as import and export price index, and it is important in policy-making. The main objective of this paper is to determine the effectiveness of the degree of the exchange rate transfer on the petrochemical products’ price relative to their share of imports from other countries. This paper is a study with a microeconomic approach about the effect of the exchange rate transfer in imperfect competition market. Therefore, this study attempts to investigate a non-competitive behavior of exporter petroleum products firms to Iran and as a result, the role of imperfect competition in the transfer of the exchange rate changes’ effect on the petrochemical industry is estimated by using ARDL model, over the period of 1983 to 2014. The findings show that there is a negative significant relationship between the market share and the degree of the exchange rate transfer on domestic prices; in other words, it decreases by increasing the share of imports.

Economic growth, inflation and stock market development are considered as three important variables in each economy, which is important to examine their relationship. Hence, the main purpose of this paper is to investigate the dynamic causal relationship between the three variables mentioned in Iran, over the period of 1981 to 2015. For this purpose, Granger causality test is used to find the causal relationship between the variables. Initially, the long-term relationship between the variables has been examined by Johansen-Juselius co-integration test, which results indicate that there is a Long-term equilibrium relationship. Then, the vector error correction model has been used to determine the direction of causality short and long-term. According to the results, the rate of inflation and the stock market development are Granger causality economic growth in the long-term. In the short-term, only there is a two-way causal relationship between economic growth and inflation, while, there is a one-way short-term causal relationship of the stock market development toward the economic growth and the inflation rate.

Due to economists’ opinion, economic corruption can negatively affect the export of industrial goods as an engine of economic growth via increasing production costs, reducing innovation and competition, reducing human capital and increasing barriers to trade. In this paper, the impact of corruption on the export of industrial goods of 53 developing countries with high and low inflation has been studied by employed two models DOLS and FMOLS, over the period of 1995 to 2014. In the short-run, for the developing countries with high and low inflation, economic corruption and inflation have negative impacts on the industrial exports, and the economy’ s degree of openness, accumulation of capital (only for countries with low inflation) and human capital have significant and positive relationships with them. The long-run estimated results are similar to the short-run results for the two mentioned groups of countries, except that the human capital is not significant for the countries with low inflation. In addition, the amount of influence of corruption and inflation on the industrial exports of the developing countries with high inflation are more than them with low inflation, it indicates that inflation accompanies corruption and declining industrial exports.

US dollar value, crude oil price and gold price are three important variables which have significant impacts on the trend of economy growth of oil producing countries. In this study regarding the interaction impact of the global market for crude oil, gold and exchange rate, the impact of oil and global gold prices shocks on the Iranian exchange rate fluctuations have been investigated using TGARH model taking into account structural break and daily data in the period 2012: 25 – 2013: 26. The results show that the amount of positive and negative shocks (good and bad news) have had statistically significant impacts, and the good news from oil and gold markets has had more impact on exchange rate. Hence, given the positive impact, can be helped to stabilization and control exchange rate by stabilizing and controlling oil and gold markets.

The main purpose of this study was to estimate the degree of international capital mobility in the developing countries during the period 2000-2014. To achieve the purpose, has been used the analysis of panel co-integration and Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), Pooled Mean Group (PMG) and Continuously-updated and Fully-Modified (Cup-FM) estimators. The result of this study showed the Feldstein-Horioka coefficient for countries studied, using the estimators FMOLS, DOLS, PMG and Cup-FM is obtained respectively, 0. 56, 0. 66, 0. 42 and 0. 64. Therefore, can be said that, on average 0. 57 percent of domestic savings spend for domestic investment in developing countries. This means international capital mobility is incomplete and low in these countries.

An accurate prediction of stock market trends is important for investors' financial decisions. Using a set of technical analysis indicators is one of the most widely used financial prediction methods. On one hand, determining the appropriate parameters for these indicators as well as their combination is a challenging decision for researchers. On the other hand, the non-linear and dynamic nature of the stock market trend lead to widespread use of nonlinear prediction methods such as artificial neural networks. Although technical analysis indicators have been used as an input of artificial neural networks, so far, the use of optimized parameters of technical analysis indicators as an artificial neural network input has not been studied. Since each stock has its own trend, using a set of default or identical parameters for all types of stocks is not accurate. In this research, the parameters of a set of technical analysis indicators are optimized using a genetic algorithm for a particular stock as input to the artificial neural network. This hybrid method is used to predict next day stock price trends. It is assumed that according to the forecast of next day trend, the investor decides to buy, sell, or hold the stocks. To evaluate the performance of the proposed hybrid method, an artificial neural network using the technical analysis indicators with the default parameters is also used to predict the stock price trend. These two methods are implemented for the actual data of Iran Khodro Company stock. Results show the superior performance of the hybrid method by 25. 1% decrease in the error of prediction.