فصلنامه پژوهش های پولی - بانکی
پیاپی 48 (تابستان 1400)

In this paper, using a general equilibrium model of overlapping generations, the impact of different financing plans of the banking industry on their risk-taking motivation is investigated. In the non-competitive banking industry, financing is done by imposing taxes on the older generation or the bankchr('39')s internal resources (bank shares). As an effective policy, this action optimizes social risk-taking. But fiscal commitment policies that tax the younger generation always increase the risk. The model predicts that in safe investments due to the existence of depositors with a uniform degree of risk, the optimal path of deposits, bank profits, expected consumption and bank value over time has an upward trend, on the other hand in investment High-risk products are the only optimal path to expected stable consumption. Also in the competitive banking industry, the optimal path of profit and consumption is expected to fluctuate in high-risk investments, but in safe investments, the optimal path of these variables has been smooth after an upward trend.

The high volume of non-performing loans and its trend over time is one of the main problems of banks and central banking in Iran. This high volume of non-current receivables and the destructive effects that this phenomenon has, has made it very important to identify and examine the role of the factors affecting it. In the present study, determinant factors from the Borrower, lender and regional economic factors affecting the non-performing loans of the country's banking network for four banking groups: commercial government, specialized government, privatized and private in 31 provinces of Iran for the years 1385-1398 has been studied. The regional dimension in the analysis of non-performing loans is the distinguishing feature of this study from similar studies that have been questioned by the authors of the article. In a geographical area, economically, the most important aspect of appearance is the density of population and the agglomeration of industrial activities, which has arisen due to the agglomeration economies, and with its expansion, the production costs of the enterprise are reduced. Therefore, the enterprises that can benefit the most from these agglomeration economies are expected to be more successful in repaying bank loans. For this purpose, data on non-performing loans, payment facilities and bank deposits for all provinces of Iran were mentioned separately for the four banking groups and data on other related variables were collected provincially. The industrial agglomeration index was also calculated using the Gorter-kok equation for the provinces of Iran. Then, using the econometric technique, the fixed effects of unbalanced panel data were determined more payment facilities, more bank deposits and higher unemployment rate cause a significant increase in non-performing loans, while higher inflation and higher industrial agglomeration reduce non-performing loans. The clearer conclusion is that the size of industrial agglomeration has a negative effect on the size of non-performing loans. These findings suggest that stress tests designed to measure the quality of banks' payment facilities and probability of default of borrowers, in addition to the capital adequacy and credit quality of banks, should also consider the impact of regional economic conditions on non-performing loans.

In the present study, the effect of exchange rate and shadow banking along with the added value of different production sectors on the overdue receivables of the banking network in the period 2008: 2-2018: 4 has been investigated using the Markov regime change pattern. The results show that the effect of research variables in different regimes of overdue receivables is different, so that if deferred receivables are in their high regime, the growth of shadow banking has a negative and significant effect, and if in their low regime Has a positive and significant effect. The exchange rate has a positive and significant effect in the high regime of overdue receivables and has no significant effect in the low regime. The added value of the oil and services sectors in all arrears regimes leads to a reduction, which shows the importance of these sectors. However, the added value of the agricultural and industrial sectors can only be reduced if the overdue claims in the regime are high. Therefore, if the goal is to reduce the volume of delinquent receivables, the necessary policy should be implemented according to the regime in which the delinquent receivables are placed and also based on the influence of the economic and banking sectors.

Knowledge and information extraction from data is an age-old concept in scientific studies. In industrial decision-making processes, the application of this concept gives rise to data-mining opportunities. Personal credit scoring is an ever-vital tool for banking systems in order to manage and minimize the inherent risks of the financial sector, thus, the design and improvement of credit scoring systems based on the data-driven and machine learning techniques have garnered newfound research interest on the subject in recent years. In the present study, important variables and parameters for credit score are identified and consequently, prediction of credit score for clients of a bank is performed. CRISP-DM is employed as the reference model for the data mining process and data modelling is accomplished based on a variety of algorithms (K-nearest-neighbors, Decision tree and Random forest). Comparative results of accuracy and sensitivity of algorithms demonstrated that the k-nearest neighbour algorithm by the accuracy of 90.3% for the training set and 76.7% for test data performs suitably to predict credit score.

Considering that the interbank market is considered as a night market to provide short-term liquidity to banks, one of the most important risks in this market - due to the short-term nature of transactions in this market - is systemic risk. Exercising this risk cycle will have devastating effects on monetary policymakers, such as the 2007-2009 crisis.  In this study, first, the buffer capital model was used to measure the systemic risk of the interbank market; and then a hyperlink-induced topic search algorithm was used to determine the penetration coefficients and systemic risk of each bank.  The research findings show that systemic risk in the Rail interbank market has increased due to the time trend. In other words, the deeper the trading volume in this market, the higher the systemic risk. Also the hub rate. And the authority of this risk in banks subject to private banks is higher than other banks. increased due to the time trend. In other words, the deeper the trading volume in this market, the higher the systemic risk.  Capital adequacy and buffer capital are important factors in limiting systemic risk. Therefore, since most banks face the problem of capital shortage, systemic risk is considered a major threat to this market.

Asymmetric information in financial markets and the possibility of encouraging bank managers to make risky choices can jeopardize the interests of investors. Financial supervision by controlling the riskiness of banks is one of the ways to protect investors. Although the main burden of financing in Iran falls on banks, overdue receivables can undermine this function. In this article, the effect of financial supervision components on credit risk of 14 commercial banks during the period 2010-2019 is investigated. Model estimation and data analysis, according to the nature of the dependent variable, ie credit risk of banks, has been done by quantile panel regression method. The results of the study show that the impact of financial supervision on the credit risk of commercial banks depends on their degree of risk; as the degree of risk of banks increases, more financial supervision variables affect credit risk. In low-risk banks, among the components of financial supervision, only the profitability index affects credit risk, but in medium-risk and high-risk banks, in addition to this index, the indicators of asset quality, liquidity quality and market risk sensitivity also affect credit risk. In high-risk banks, credit risk is also affected by the capital adequacy ratio. It is also inverse to affect all of the above variables on credit risk in different quantities.