JOURNAL OF ECONOMIC MODELING
Year:2018 | Volume:3 | Issue:4 (11)|Papers Count:6

The dynamics of capital markets is one of the main effective parameters of economy growth of each country. Selecting of optimal collection of properties is one of the capital market theories that has the certain importance in economics. The main aim of this research is solving stock portfolio-optimization using of Imperialist Competitive Algorithm. An applied pattern is developed model of mean-variance, mean-semi variance, mean-absolute deviations and Mean-Conditional Value at Riskthat its limitation has been added. In the purpose of solving problem of optimization of investment basket, we used of 25 daily accepted stock in Tehran Stock Exchange between 2009-2016. The results of 4 patterns portfolio of research show that in the Imperialist Competitive Algorithm (ICA), mean-Conditional Value at Risk has high accuracy optimization in compare of others.

Estimation of conditional variance has lots of application reflecting economic, especially financial economics, social economics and political economics’ risk and volatility research. Therefore, obtaining accurate estimation of the conditional variance is especially important. Recently Hansen has modeled the conditional variance and realized volatility simultaneously which is known as Realized GARCH model. In this paper, we introduce a fuzzy coefficient in the Realized GARCH, and then compare this model with GARCH, EGARCH and GJR-GARCH methods as well as the RGARCH model with 2 different criteria of the realized volatility concerning Tehran Stock Exchange Index. The log likelihood value used to evaluate in-sample fitting. According to this criterion, our proposed model has a better fit than the rest of the models. To evaluate the accuracy of prediction of conditional variance, the rolling window method used with two MSE and QLIKE loss functions. The results indicate that our model, the Realized GARCH with fuzzy coefficient has the best performance with both loss functions.

The main purpose of this paper is to present a more efficient method than the 69th act and previous studies, for determining risk factor of Market Risk Capital Requirement. In this regard, by using statistical data of TEPIX and Value at Risk (VaR) different approaches, we modeled the stock investment market risk in insurance companies without asset liability mismatching (ALM) risk. Next, by calculating duration gap of asset and liabilities, we calculated Market Risk Factor with (ALM) risk. The findings indicate that first; Monte Carlo simulation based on the TGARCH model is more efficient than the other model and second; final market risk factor are calculated 34. 2% and 36. 8% for life and non-life insurance policies, respectively.

This paper investigates the effective factors on migration network formation among Ardabilian who migrate to Sari. Instead of using proxy variables for relationship, direct relational data are recognized by conducting survey. By employing the valuable data and based on econometric model of network formation and applying logit method for dyadic data, effective factors on network formation are studied. According to strategic view on network formation, it is expected that form a star shape network among migrants. Moreover, it is expected that migrants not only make connection based on strategic behavior, but also based on economic and social similarities. Therefore, variables such as time of arrival, distance at birthplace are used as explanatory variables as well as variables such as age, education and income as proxy for homophily are employed as control variables. Results show that time of arrival, distance at birthplace, age, education and income has significant effect on probability of making link. Based on results, migrants don’ t tend to linking with earlier migrants. The probability of linking increases among migrants who migrants between 6 to 28 years, specifically between 11 to 16 years. This means that earlier migrants are important in migrants’ network, but earliest migrants are not attractive for making connection.

This paper examines the new indicator of Zenga inequality, which is based on the ratio between the lower and the upper group means. For analyzing this criterion, income data of Iranian households during the period of 1378-1396 was used. We evaluate the performance of the Gini and Zenga measures in analyzing household income data in comparison to each other. Within the comparison between the Gini and Zenga indices and related curves in household income data in Iran, we have observed the Zenga estimator has desirable properties than the Gini index. The Zenga curve is more explanatory and more flexible than the traditional Lorenz curve. The Zenga inequality index in the estimation of income inequality shows a similar trend for urban and rural areas relative to the Gini index. This indicates that the Zenga index is consistent with the Gini coefficient.

In this study, using the smooth transition regression (STR) and quarterly data during the period of 1997: 1-2015: 3, the nonlinear monetary policy functions of the Central Bank of Iran, regarding the risk of financial markets, be estimated. The results indicated that the risk of financial markets is an important factor in regime switching of monetary policy in Iran. By passing the risk of financial markets from a threshold level, monetary policy is focused on adjusting the output gap so that the coefficient of output gap is consistent with the Taylor rule in the second regime while in the linear part (the first regime) it is in contrast to the expected coefficient. This results indicate the variability of monetary policy behavior in various financial market risk situations. However, the coefficient of inflation gap in both regimes and both scenarios is not statistically significant.