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Showing 4 results for Credit Risk

Amir Reza Soori, Dr Ahmad Tashkini, Mohammad Reza Saadat,
Volume 1, Issue 2 (12-2010)

The main purpose of this paper is to examine the effect of merger, concentration and credit risk on the efficiency of Iranian Banking industry. To measure the efficiency of Iranian banking system, we have used the data of commercial & specialized bank's balance sheets during 2001-2007, and a parametric approach to estimate two empirical models. To estimate efficiency measures and determining main factors affecting the measures, we have used a Logarithmic - Linear form of a random Translog cost function. The results of the first estimated efficiency model show that the average efficiency measure of banking system in Iran is 54% and that the merger of the more inefficient banks within the efficient bank will cause the average efficiency measure rise to 70% The results of the second model - assessing the effecting factors on efficiency- show that the efficiency of banks has an inverse relationship with the concentration (competition in banking industry), and a direct relationship with the IT index (e-banking activity) and the facilities to assets and capital to assets ratios (as the indices of the credit risk).
Parviz Rostamzadeh, Ruhollah Shahnazi, Mogammad Sadeq Neisani,
Volume 9, Issue 32 (7-2018)

Credit risk is due to that recipients of the facility, deliberately or involuntarily, don’t have ability to repay their debts to the banking system that this risk is critical in Iran compared to the global. Therefore, the purpose of this study was to investigate the effect of macroeconomic variables on credit risk of Iranian banking industry during the 2006-2016 years and also simulation and prediction of credit risk situation in 2017 under different stress scenarios, bu using stress test. Data used in this research is time series and seasonal. In order to implement a stress test and achieve the purpose of the research, first, the effective macroeconomic variables and the rate of each one's influence on the credit risk are determined using Auto-Regressive Distributed Lags (ARDL). Accordingly, the inflation rate, exchange rate, unemployment rate and housing index in total have a positive effect and variables GDP, the interest rate of bank facilities and the volume of concessional facilities to both government and non-governmental sectors, have a negative impact on credit risk. In the following, using the stress test, simulation of critical situations and prediction of credit risk values in 2017. This was done in three scenarios with titles of mild stress, extreme stress, and hyperstress that in each scenario, different shocks are applied to the variables affecting credit risk. The results of the stress test and scenarios show that the compulsory reduction of interest rates on bank facilities in all three scenarios, initially in the second quarter of  2017, leads to a reduction in credit risk, but rising exchange rates, rising inflation, falling economic growth, as well as accumulation of past values of credit risk, has led to a rapid increase in credit risk and also in scenarios with more severs shocks, has led to catastrophic increase of credit risk in later periods in all scenarios.

Hassan Dargahi, Mojtaba Ghasemi, Sajjad Fatollahi,
Volume 11, Issue 40 (6-2020)

This study investigates the relation between bounced checks and economic growth through the banking credit risk channel by estimation of a simultaneous equation system with panel data for 31 Iranian provinces covers the years from 2011 to 2015. For this purpose, after identifying determinants of the bounced checks, the relations of this variable with the non-performing loans, banking loans and economic growth are evaluated. The results confirm the positive relationship between the bounced checks to GDP ratio and the prices index, whereas the impacts of output deviation from trend and the index of enforcement of laws on the bounced checks are negative. In times of stagflation, with the decreasing possibility of defaults, the bounced checks tend to grow. Also, with the development of legal and judicial system in the country with a view to boosting institutional and governance quality, the number of bounced checks decreases on the scale of economic activities. On the other hand, the number of bounced checks after fixing the control variables will lead to an increase of non-performing loans and the bank credit risk. Meanwhile the impact of bank loans on economic growth through the productivity channel is meaningful and positive. Therefore, in the Iranian economy the increase of bounced checks through the channel of bank loaning power will have a negative influence on economic growth.

Nasrin Motedayen, Rafik Nazarian, Marjan Damankeshideh, Roya Seifi Pour,
Volume 12, Issue 45 (11-2021)

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.

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