JOURNAL OF ECONOMIC MODELING
Year:2019 | Volume:4 | Issue:1 (12)|Papers Count:6

The wide range of impact has transformed economic growth into one the most important key variables in macroeconomic theories. In this survey, according to the hypothesis and empirical results of Grilli & Feretti (1995), the effect of financial integration on the growth of selected developed and developing countries during (2000-2010) has been examined. For this aim, panel data based on generalized method of moments (GMM) is used to examine the model. This survey showed that the effect of financial integration (net foreign assets as a measure of financial integration) on economic growth in two groups of surveyed countries is not the same. Based on the results of this survey, the financial integration in the selected countries of the Organization for Economic Cooperation and Development (OECD) has a positive effect and in the selected countries of the Middle East and North Africa has a negative effect on economic growth. Therefore, it seems that the level of financial market development and the degree of homogeneity of these markets between countries play a decisive role in the impact of financial integration on economic growth.

The present study aims to provide a model for covering price risks in Iranian importers of agricultural commodities. In order to assess the efficiency of the model, daily spot and futures prices of soybeans and corn were collected from the Chicago Mercantile Exchange (CME) during the period 05/01/2010 to 06/08/2018. The data were analyzed for two situations as follows: 1-Futures contracts can be traded. 2-Futures contracts cannot be traded. One of such methods in the dynamic state is Markov switching GARCH model. The application of the Markov switching GARCH model produced a high volatility and a low volatility regime. In case the quasi futures contracts are used, the optimal risk coverage ratio for the soybean and corn products would respectively be equal with 0. 849 and 0. 9065; while the efficiency of the risk coverage would be respectively equal to 82 and 78 percent for these two products.

Financial stability is firmly considred as a supplementary goal in the monetary policy environment post 2007 financial crisis. New monetary policy that contains monetary thriple goals-price stability, sustainable growth and financial stability-in the context of optimum policy rule is highly recommended post crisis although some policymakers are still interested in utilizing the convetional approach based on discretionary measures given the restrictions in macroeconomic structural spcification. In this regard, a Vector Auto Regression under Policy (PVAR) approach is technically applied as an alternative approach to resolve the specification challenge. The PVAR approach is also utilized to estimate monetary policy optimum reaction function given the financial stability. The model output indicate that the policy reaction function significantly affcets the triple goals so policymaker reaction function reduces price and financial instability along with more sustabible growth.

The purpose of this study is to study the financial interactions between financial markets of selected financial partner of Iran, including China, France, Germany, Italy and the UAE. For this purpose, the VAR model has been used. Financial risk data is collected annually (from 1984 to 2017). The results indicated that there was a positive and two-side relationship between the financial risk fluctuations between Iran and China, the UAE and Italy, the United Arab Emirates and China during the period under review. In addition, financial risk aversion was observed one-side from Iran to Italy and the UAE to France and China to the UAE. Also, financial risk aversion from Germany to China, Iran, Italy, and the United Arab Emirates was seen one-side, in other cases, there was not Fluctuation overflow. Ultimately, it could be said that Germany was only country affected by risk of the other countries.

Sometimes, in econometrics problems the observations are not independent, so that their dependence is due to the location of observations in the studied space. To analyze these data are used the spatial regression models. Due to the large number of parameters in these models are used the iteration algorithms to obtain the maximum likelihood estimations, so that it encounters problems such as the complexity of the calculation. In addition in economic studies, the number of observation is large and it seems useful to use the Bayesian approach. The main purpose is using the Bayesian and the Likelihood approaches to estimate the parameters of the three spatial econometric models. Then, comparing the performance of these two approaches, as well as comparing the performance of these three spatial regression models and finally the three models are implemented on two real data. It is observed that the results of the Bayesian approach are more credible than the likelihood approach in these type of econometric models.

With the expansion of mutual dependence of financial markets around the world and the transfer of effects from market to market, in recent years financial markets of developing countries have been interdependent. These conditions have led investors who are trying to diversify their assets in other countries equity markets to pay particular attention to stock markets. The aim of this research is to study the stock markets interdependence of selected developing countries Including Iran. Countries studied are Iran, Kuwait, UAE, Indonesia, Saudi Arabia and Oman for the period 2001-2017. The panel gravity model was used for data analysis. Our findings showed there is a positive relationship between market size, financial integration and interdependence of the stock markets. Also, Oil price uncertainty and information capacity had the negative effects on stock markets interdependence.