fatemeh abdolshah; Saeed Moshiri
Abstract
Because of prevalence of non-performing loans in Iranian banking sector, it is important to estimate the default probability of borrowers in order to effectively manage credit risk. This paper conducts stress testing for default probabilities in banking industry of Iran. We apply the credit portfolio ...
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Because of prevalence of non-performing loans in Iranian banking sector, it is important to estimate the default probability of borrowers in order to effectively manage credit risk. This paper conducts stress testing for default probabilities in banking industry of Iran. We apply the credit portfolio approach model developed by Wilson (1997) and analyze the impacts of various macroeconomic shocks on default rates of banks. In the constructed model, we first estimate the effects of macroeconomics variables on default rate. Then the dynamic relationship between selected macroeconomics variables is estimated by a VAR model. Residuals obtained in the two previous steps were used to construct the covariance matrix for system of equations. Finally, using the Monte-Carlo method, a path of default probabilities is simulated in a one-year horizon under different scenarios. We compare default rates under different stress scenarios with baseline scenario to identify the effects of different shocks. The results of simulation show that unemployment rate shock has been the most harmful factor for default probabilities, followed by exchange rates shock. A shock to GDP growth also affects default rates significantly. Inflation shock generates the least important effect on default rates, consistent with the insignificant coefficient of inflation rate in the estimated default probability equation. A simultaneous shock to all macroeconomic variables has higher impact on the default rates in lower tails than upper tails. The results also show the effects of shocks decrease with the passage of time.
Teymour Mohammadi; Farzad i Eskandar; Davoud Karimi
Abstract
The purpose of this study is to investigate the effects of macroeconomic and bank-specific factors on non-performing loans for the period of 2005 to 2013. A dynamic panel data model is used in 18 banks and to assess non-performing loan, the ratio of non-performing loans to all granted loans has been ...
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The purpose of this study is to investigate the effects of macroeconomic and bank-specific factors on non-performing loans for the period of 2005 to 2013. A dynamic panel data model is used in 18 banks and to assess non-performing loan, the ratio of non-performing loans to all granted loans has been utilized. The results of Generalized Method of Moments (GMM) indicate that among all considered macroeconomic variables, economic growth has a negative effect, the gap between real interest rate in informal market and the real interest rate in formal market and also exchange volatility have positive effect on the ratio of NPLs to all granted loans. The results of bank-specific factors show that capital adequacy ratio, deposit to expenditure ratio, as an indication of economic efficiency, and share of each bank in total loans granted, as a proxy for banks' size, all have a significant negative influence on non-performing loans. The result confirms that “Bad Management hypothesis”, in which the increase of total expenditure efficiency leads to reduction in non-performing loans and “Market Strength and Stability hypothesis”, in which the banks with higher market power has less due date non-performing loans are both confirmed.
Mohammad Jelodar Mamaghani; Abdosadeh Neisy; Mahdi Goldani; Saeed Rahimian
Abstract
Recent decade was undoubtedly a uniqe one for the banking and financial sector in Iranian economy. Stock market index was breaking records now and then, new credit institutions were established one after another, and different banks were competing in raising their interest rates of deposits. Put this ...
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Recent decade was undoubtedly a uniqe one for the banking and financial sector in Iranian economy. Stock market index was breaking records now and then, new credit institutions were established one after another, and different banks were competing in raising their interest rates of deposits. Put this story alongside an unprecedented bubble in construction sector in years 2006 and 2007, and we can realize that real sectors of the economy, specially industry and agriculture, were in what circumstances. Whatever is our definition of development and whatever is our index for measuring it, we cannot deny the fact that the reliable development is the one which is balanced and can cause growth in all sectors in a homogeneous and proportional way. One of important factors in analysing the situation of these sectors, is the credit ranking and grading that they have been able to get based on their performance from banking and financial system. Therefore, measuring credit risk in these sectors can make a good impression on their performance for policy-makers in each sector and economist involved with the issue. In this paper, we are going to calculate and analyze credit risk in different sectors of Iranian economy, namely “industry”, “agriculture” and “services and housing” sectors by analysing companies accepted in Stock Exchange and OTC markets. Some of the results of this study are high volatility and declining credit risk in industry sector, high and growing volatility in services and housing sector, and low volatility but very high average and declining trend in agriculture sector.
Mir Fize Fallah Shams; Hamid Mahdavi Rad
Volume 12, Issue 44 , April 2012, , Pages 213-234
Abstract
Nowadays, leasing industry is recognized as one of the strategic options in economic development. Leasing companies have a great profitability and are faced to the most risks. Credit risk, business risk, residual value risk and exchange risk are some of important risk in leasing companies. Among them, ...
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Nowadays, leasing industry is recognized as one of the strategic options in economic development. Leasing companies have a great profitability and are faced to the most risks. Credit risk, business risk, residual value risk and exchange risk are some of important risk in leasing companies. Among them, credit risk is the most important. Subsequently, making logic relationship between risk and return is the essential element to devote optimally resources and to guarantee profitability leasing companies.
In this paper, on the basis individual costumers data extracted from Leasing Iran Khodro Company database (since1381-1384) and using tow sample t-student test, and determinant coefficient, we found five variables as factors affect credit risk. They include, net monthly costumer income, loan time, loan amount, net monthly guarantor income and experience. In addition, we apply tow credit risk models (Logit & Probit) for leasing loans. The results of Wald, Log likelihood and Wilk΄S Lambda tests indicate that the efficiency in Logit model (%98.39) is more than Probit model (%97.44).
