نشریه اقتصاد محاسباتی
سال دوم شماره 1 (زمستان 1401)

The efficiency and performance of the stock market in mobilization of financial resources because of the impact on the amount and quality of investment has great importance in the economy. Although competitiveness is a set of institutions, policies and factors that can create a high return on production factors, including capital by maintaining and deepening in business, industry and the country. Therefore, competitiveness is one of the determinants of the returns of stock companies and, consequently, the mobilization of financial resources in the stock market. For this purpose, the present study examines the effects of competitive components on mobilization of financial resources in members of World Federation of Exchanges with using panel data and Generalized Method of Moments(GMM) data from 2007 to 2019.The results of the research show that innovation-driven factors (INV), efficiency-driven factors (EFF) and basically-based factors (BAS) have a positive and significant effect on investment flow of the stock market financial resources. Also, liquidity of the stock market and real interest rate have a positive and significant effect on investment flow of the stock market financial resources.

In the broadest sense, sustainability means the ability to maintain or support a process continuously over time. To maintain the sustainability and sustainable development of society, we need to meet the demands of today's generation without compromising the needs of future generations. Sustainability has three dimensions: economic, social and environmental, the latter having the most decisive weight. In the 2000s and 2010s, multiple experimental studies were conducted on sustainability employing an experimental model based on the Kuznets curve. Moreover, in recent years, the two concepts of human development and sustainability have been extensively addressed in the development literature. Various studies have considered human beings as a substantial factor in the development of different societies. The composite index of human development has various dimensions that may affect sustainability and specifically environmental sustainability. Plus, exorbitant oil revenues in oil-producing and exporting countries have had a significant impact on human development in these countries. Therefore, we can classify these countries into three groups of oil-rich countries with high human development, oil-rich countries with medium human development and oil-rich countries with low human development. In this paper, to investigate the impact of the human development index in 35 major oil-exporting countries around the globe, we applied the panel data method and Kuznets function for the data from 2010 to 2019. The result of the econometric model estimation demonstrates that the greater the human development index, the higher the environmental sustainability in these countries.

The purpose of this article was to investigate the replacement of traditional money with virtual currencies and its effects on macroeconomic variables with the approach of Dynamic Stochastic General Equilibrium (DSGE) models. For this purpose, the data of the period 2018-2019 with seasonal frequency have been used. In the model designed in this article, it is assumed that due to the use of virtual money, a substitution between virtual money and traditional money will happen in people's asset portfolio. In this study, the shock caused by the price and volume of Bitcoin transactions is considered as an indicator for the demand for virtual currency. The results show that the shock from virtual currencies has led to a decrease in the demand for traditional money, in other words, there has been a substitution between holding traditional money and virtual money. In addition, the results indicated that due to the shock of virtual currencies, the amount of consumption in the economy has increased, and on the other hand, the amount of government income from royalties and money printing has decreased. Also, the results showed that the government's tax revenues have also decreased due to the trend of financial resources in the economy towards the demand of virtual currencies.

The probability of default is the degree of certainty that a particular bank will default or that the counterparty will not repay its obligations according to the agreement. This article seeks to calculate the default probability of sample banks in the banking network of Iran, for this purpose, using the new approach of the systemic model of bank losses and the Monte Carlo simulation method, to calculate the probability of bank default in the two cases of the presence and absence of contagion effects between Bank is paid. The sample includes 15 Iranian banks and the time period of 2017. The results indicate that in the studied sample, the situation of banks' capital is not favorable for covering leading risks, the probability of bank defaults has a negative and significant relationship with the criteria of banks' excess capital, and with the increase of inter-bank correlation, a kind of There is a cluster effect of bank defaults, and the default of one or more banks can lead to a banking crisis and the collapse of the entire banking system.

The main purpose of this paper is to investigate the factors affecting exchange rate and to forecast the future trend using econometrics and machine learning methods. The seasonal moving average of IR Rial to US Dollar rate from spring 1392 to spring 1401 is the proxy of exchange rate as the dependent variable and balance of trade, debt to GDP, GDP, inflation rate, liquidity volume, OPEC oil price and unemployment rate are considered as the independent variables. For determining the factors influencing the exchange rate, Sobol' Sensitivity Analysis, a machine learning method, is used and VECM is harnessed for regressing and forecasting. Variance decomposition and impulse response function are also utilized as instruments of VAR for measuring the future shocks effects of independent variables on exchange rate and the periods in which these shocks will be neutralized. The results show that liquidity volume, oil price, unemployment rate and inflation rate are the most significant determinants, respectively and the future shocks of liquidity volume, inflation rate, oil price and unemployment rate have the longest period of impacts on the exchange rates, respectively. The study outcomes also show an affinity between econometric and machine learning results in this case. The predicted amounts of exchange rate until winter 1402 are also included in the results.

The aim of the current research is to obtain the demand function of production inputs in the petrochemical industry. In order to test the relationship between the variables, the information of the time period 2015-2018 has been used. The statistical population of this research is the companies that produce petrochemical products. The results of this research showed that the relationship between the share of raw material production factors in the total cost paid to production factors and the relative price of labor, raw materials and raw materials and products is positive and its relationship With the relative price of machines. and energy is negative. The results of substitution elasticities also show that capital with labor, raw materials with labor and capital with energy are alternative inputs, capital with raw materials, raw materials with energy are complementary inputs. In the estimated cross elasticities, see It was found that the price elasticity of raw materials is higher than other inputs in terms of absolute value, in other words, the demand for raw materials is more sensitive to price changes than other inputs. The inputs of capital, labor and energy are in the next ranks in this field. Also, the absolute value of the numerical value of the price elasticities of all inputs is less than one, and therefore it can be said that the demand for all inputs is inelastic.