JOURNAL OF ECONOMIC MODELING
Year:2018 | Volume:3 | Issue:2 (9)|Papers Count:6

The main aim of this study is to investigate the possibility of hedging the risk of exchange rate fluctuations by using the gold future market and comparing the risk hedge in Tehran Exchange Stock as a developing financial market with the Istanbul Exchange stock as a newfound financial market. In order to access the research goal, daily data from December 13, 2007 to April 30, 2018 was used for Iran and March 18, 2013 to August 17, 2018 used for turkey and the Markov-Switching Model was used. The results of this study showed that the coefficient of the future price of gold coins for zero regime (low swing) was 0/0013. For regime one (much swing), the future gold price coefficient was 0/0046. On the other hand, the results of this study showed that the coefficient for future changes in gold prices for the Istanbul Exchange in zero regime (low swing) was 0/00061 and for regime one (much swing), the coefficient of future price changes of gold was 0/0075.

According to the importance of the hedging of gold coin market fluctuations, the purpose of this study is to estimate the minimize variance of optimal hedge ratios for Bahar Azadi coin futures contracts from period of 2013/12/17 to 2017/06/01 using Markov Switching model and comparison of hedging performance computed by it with other commonly used model in this field. For this purpose Effectiveness of Markov Switching dynamic model of optimal hedge ratios and static optimal hedge ratio of Ordinary Least Square model is compared, in two periods in the sample and out of the sample. The results indicate that in the sample period, the hedge ratio of Markov Switching model had the best performance in terms of reducing variance and increasing utility rates. Also in out of the sample period indicated that the superiority of the Markov Switching model against simple hedge depends on the period and the perspective of the investor.

Designing an appropriate industrial clusters model as a general method in the regional economy is an important problem for economic and industrial development planners. Paying attention to the objectives of sustainable development and the green economy, in addition to the traditional objectives of economic growth, is the need of countries today. In order to meet the goals of sustainable development, the multi-objective mathematical programming and dynamic model of industrial clusters was designed and tested to optimize four objectives: profit, employment, cost of transportation of materials and environmental appraisal score of the cluster. In designing parts of the model in the assignment of firms and the establishment of relations between and within cooperation networks, the concepts and research literature of cellular manufacturing were used because of proximity of the concepts and extent of studies in this field. The problem solving results by decomposition methods and weighting functions objectives with experts' opinion and analysis of experiments confirmed that the optimal results of experiments have been presented 99% improvement in minimizing the metric function of the model.

The main purpose of this paper is to examine the effect of trade openness and government size on macroeconomic volatility. Economic theories do not clearly show the effects of trade openness and government size on macroeconomic volatility. Therefore, this is essentially an empirical problem. Hence, we have presented an empirical model to test the effect of trade openness and government size on macroeconomic volatility in Iran over the period of 1352-1395. For this, first, the macroeconomic uncertainty was extracted using stochastic volatility model with leverage effects by technique of principal component analysis. The results showed that in the long run, trade openness and government expenditures have a positive effect on macroeconomic volatility in Iran. In addition, in the short run, there is no significant relationship between the relevant variables and macroeconomic volatility.

Uncertainty in oil markets has led economic researchers to the use of stochastic processes. The purpose of this paper, is the use of stochastic differential models to predict the crude oil price of West Texas Intermediate (WTI) and compare the forecasting performance of these models with ARIMA and GARCH models. In this paper, daily data of WTI crude oil prices from 2/01/1986 to 10/17/2016 has been used that the period 2/01/1986 to 29/08/2016 has been used for estimation in-sample and the rest of the observations have used for out of sample forecasting. The results of the comparison of prediction models using RMSE has shown that long-term memory models (Arfima-Figarch) and stochastic differential models are more accurate forecasting performance compared to ARIMA and GARCH models in in-sample and out of sample forecast for 5-days, 10-days, and 22-days horizons.