Journal of Economic Modeling Research

Impact of Economic Complexity and Political, Economic, Financial Risk Shock on Income Gap; Application of PVAR Model

Hamid Sepehrdoust

The status of income distribution is economically important because other macroeconomic variables, especially savings rates, affect the amount of investment and aggregate demand in different markets, and are politically a measure of government efficiency in attracting voters. The present study aims to investigate the macroeconomic variables affecting inequality in income distribution in the two groups of middle-income countries and high-income countries based on the International Monetary Fund classification. For this purpose, the annual data of economic complexity, scientific productivity, political risk, economic risk, and financial risk and the period 2019-2000 and the panel method have been used. The results show that in high-income countries, increasing economic complexity and scientific productivity reduces income inequality, while in middle-income countries, increasing scientific productivity reduces income inequality, but increasing economic complexity increases income inequality. Reducing political risk in both groups reduces income inequality; While reducing financial risk reduces income inequality in high-income countries, it increases income inequality in middle-income countries. The impact of economic risk on income inequality is also negligible in high-income countries, while in middle-income countries the impact of economic risk on income inequality is very strong, and reducing economic risk in this group of countries strongly reduces income inequality.

Investigating the Factors Affecting Housing rent in Urban Areas of Iran with Emphasis on Urban Grouping

Hojjat izadkhasti

Rental housing has been affected by housing prices in different periods and the growth of housing prices has reduced the purchasing power of housing applicants and increased the percentage of rented households. Therefore, any recession and boom in the housing sector has a direct impact on the housing rental market, and planning to control the rental market will not be achieved without considering the housing market. In this regard, the purpose of this study is to investigate the factors affecting housing rent based on two groups included large, small and medium cities in Iran using the Generalized moment method (GMM) in the period (2008-2018). The results show that housing rental prices in the previous period, housing prices, land leverage and real per capita income of urban households had the most positive impact on housing rents in both large and small and medium cities. Also, the impact of housing prices and rental prices in the previous period has been greater in large cities. Also, Housing bank facilities, the number of urban marriages and the real interest rate were other variables affecting the rental price of housing in urban areas.

Modeling Valuation of Companies Based on Technology and Innovation a Case Study on Gamron Petro Industry Co.

Ali Nazemi

Achieving reality-based valuation of innovative companies is an undeniable challenge for the founders and investors of innovation. The purpose of this study is to model a logical, innovative and scalable approach to valuing innovative companies. In this way, by selecting the Earning Before Interest and Tax (EBIT) of the studied innovative company, as a state variable and simulating its future income flows based on Arithmetic Brownian Motion (ABM) standard and using the framework of Real Option Valuation (ROV) method, the valuation model was created. The accuracy and efficiency of this model was proved by extracting the data of the fiscal years from 1392 to 1395 of Gamron Petro Industry Exchange Company and comparing the results of the model with the market value of the company in Tehran Stock Exchange. On the other hand, in order to test the effect of real interest rate on the model results, by defining three different values of real interest rate, the effect of real interest rate fluctuation on the model evaluation results was investigated. Thus, the high flexibility of the model using the method of real option valuation is fully reflected in the research results.

Investigating the Eeffects of Factors on Capital Adequacy Ratio in the Islamic Banking System of Iran and Malaysia by Threshold Regression Approach

Azadeh Mehrabians

Capital adequacy ratio is one of the most important indicators in analyzing the situation of banks in order to manage banks against risks such as bankruptcy and their inability to meet obligations. This controls the risk management of banks. The aim of this paper is to investigate the effect of banking variables on the capital adequacy ratio (CAR) in private banks in Iran during the period 2011-2018 and in Malaysia quarterly during the period 2012:01-2019:04 by Threshold Auto regression Method. The results showed that the CAR in the low regime with four lags had a negative effect and in the high regime had a direct effect on the CAR of Iranian banks. But it did not have a significant impact on the Malaysian banking system. The share of bank deposits in Iran in both regimes has a negative effect on the CAR. But it had a direct effect on the Malaysian banking system in the high regime. The size of the bank in the low regime had a direct effect on the CAR of private Iranian banks. But in Malaysia, in both regimes, it had a direct impact on the capital adequacy ratio. The share of credits in both regimes had a direct impact on the CAR in Iran. But in the Malaysian banking system in both regimes had a negative impact on the CAR. Liquidity in the low regime has a negative effect on the CAR in private Iranian banks. But in the high regime did not have a significant effect. While in the high regime, liquidity has a direct and significant effect on the CAR in the banking system of Malaysia. Returns of assets in the low regime do not have a significant effect on the CAR of Iranian banks. But returns of assets in the low regime have a direct and significant effect and in the high regime have a negative effect on the CAR in the Malaysian banking system. Financial leverage in the low regime does not have a significant effect on the CAR of Iranian banks, but in the Malaysian banking system in the low regime has a negative effect and in the high regime has a direct effect.

The Asymmetric Effect of Macroeconomic Variables on Stock Price Index: Quantile ARDL Approach

amirali farhang

This paper aims at investigating the asymmetric impact of long-term and short-term macroeconomic variables on the capital market prices of Iran.Macroeconomic variables are inflation, exchange rate, non-oil trade balance and crude oil prices. In order to investigate these relationships, the quantile autoregressive distributed lag (QARDL) method introduced by Cho et al. (2015) has been used. For this purpose, monthly data related to Iran's economy in the period 2008: M9-2021: M6, have been used. Findings show that in the short run, the macro variables used except the trade balance and oil prices have an asymmetric effect on the capital market price index. In the long run, all variables except oil price have an asymmetric effect on the stock price index and the effect of oil price is symmetrical and significant. This conclusion shows that in situations where the stock market price index is in a state of prosperity, recession or normal, except for oil prices, the effect of research variables on this index is not the same and even this effect is different in the short and long term.

Designing a Comparative Model of Bank Credit Risk Using Neural Network Models, Survival Probability Function and Support Vector Machine

Rafik Nazarian

Credit risk is the probability of default of the borrower or the counterparty of the bank in fulfilling its obligations, according to the agreed terms. In other words, uncertainty about receiving future investment income is called risk, which is of great importance in banks. The purpose of this article was to estimate the credit risk of Mellat Bank's legal customers. In this study, the statistical information of 7330 real customers was used. In this regard, the results of neural network model and support vector machine model have been compared. The obtained results have shown that the components considered in this study based on personality, financial and economic characteristics had significant effects on the probability of customer default and credit risk calculation. Also, the results of this study showed that the application of control policies at the beginning of the repayment period suggests facilities that have the highest probability of default with long life and high repayment. Comparing the results obtained from the prediction accuracy of different models, it was observed that the explanatory power of the support vector machine model and the use of the survival probability function was higher than that of the simple neural network model for the studied groups of real customers.