فصلنامه پژوهش های پولی - بانکی
پیاپی 38 (زمستان 1397)

The purpose of this paper is to design a prediction system for thresholds of the bankruptcy of banks based on the business cycle and examine the effects of different approaches in defining the bankruptcy threshold in predicting bankruptcy time of Iranian banks using the Kaplan-Meier and Cox Proportional-Hazards Models. So, the data of listed banks in Tehran Stock Exchange were used from 1385-1395. The results indicate that Iranian banks are affected by five leading variables, which bank supervisors can identify risky banks using these indicators. These variables are operating profit to operating costs ratio, the ratio of total net income of interest and operating income to the total assets, the income of banking services to total income ratio, the ratio of administrative and general expenses to total expenses and bank’s size. Although, results indicate that base on AIC, Z-score approach is the best criteria for defining and identifying banks' bankruptcy thresholds in comparison with capital adequacy ratio and non-performing loans ratio. Based on this approach, at overall, banks will be on the thresholds of bankruptcy for up to 7 years and this will be reduced to 5 years in recession and during the boom, banks' bankruptcy thresholds will increase to 8 years.

After the recessions and recent economic crises (especially The Great Recession), many policymakers, economists and researchers have done the theoretical and empirical studies under the conditions of banking and financial crisis or various credit conditions to find out that how monetary and financial policies affect the macroeconomic system. This paper investigates the effect of fiscal policy shocks (positive and negative) on GDP during the credit cycles employing seasonal data of Iranian economy during the period 1990:Q2–2018:Q2 via asymmetric threshold vector auto-regression (TVAR) model. We used the Lee-Strazicich and nonlinear TAR unit root tests and the Johansen-Juselius and Saikkonen-Lutkepohl co-integration tests to investigate the degree of integration and co-integration of variables. The results of estimating TVAR model, considering the growth of bank credits to the private sector as a threshold variable, implies that the positive shock of government expenditures is more effective during the credit recession Than Positive while the negative shock of government expenditures is more effective during the credit boom Than negative. Besides, the positive shocks of tax on GDP are more effective in the period of bank credit boom Than negative while the negative shocks of tax on GDP are more effective in the period of bank credit recession Than positive. Hence, there is asymmetry in the effect of positive and negative shocks of fiscal policy during the credit cycles for the Iranian economy.

There is a lot of evidence on the relationship between business cycles and financial. We study the credit cycles of the Iranian Economy and their relationship with the business cycles. The literature suggests using various indicators for credit cycles. We use the ratio of banking loan to the private sector to potential nominal GDP as the indicator of credit status. Contraction and expansion period are identified after removing the long run trend and short-term fluctuations. The correlation between the cycle of credit status indicator and the business cycles indicator is more than 41%. Statistical analysis of the concordance of the periods of recession and expansion of macroeconomics (business cycles) with the periods of contraction and expansion of credit show 70% concordance. When we limit the results to the 1376-1393 (1997-2014) period, credit contraction leads recession for one quarter. In the end, we study the credit status in different economic sectors and also in banking system resources (deposits). Credit cycles of economic sectors and deposits cycle show that real sector volatility may cause credit cycles through deposit volatility.

In the knowledge-based economy, intellectual capital is used to create value for organizations. Organizations seek to create, manage, expand and exploit optimal intellectual capital in terms of organizational value creation and business process improvement. Considering the competitiveness of the present age seems that banks must improve their performance to achieve their goals and perform their duties correctly so that they can surpass their competitors. One of the factors affecting the performance of banks is intellectual capital. The purpose of this study is to investigate the effect of intellectual capital and the value-added intellectual coefficient on the performance of accepted banks in Tehran Stock Exchange from 2012 to 2016. To test the hypotheses of the research, multiple linear regression analysis of panel data has been used. One-time of intellectual capital is generally reviewed and once the effects of intellectual capital components were examined. The results of the research show that intellectual capital is more influenced by the human capital and the value-added human capital coefficient has a positive and significant relationship with the performance of accepted banks in the stock market.

One of the most common causes or credit phenomenon that is taken into account for credit risk is the customer’s noncompliance with the commitments. Thus, by predicting the behavior of loan applicants, the growth rate of debts can be decreased. Hence, this study is conducted on corporate applicants for loans in one of the public banks in Iran. In this paper, the main elements comprising the customers’ behavior are selected with the help of categorized sample collection of 521 random samples from all corporate applicants. the process of data preparation, then, is accomplished by summarization, integration, and interpolation of some lost data. In the next step, 85 key performance indicators are selected for modeling. In order to measure the importance degree of the affecting elements on the customers’ behavior, the decision tree، neural net algorithms and Support Vector Machine were applied, the decision tree algorithm with 14 percent average absolute error, having the highest degree, was recognized as the top algorithm capable of assessing the probability of defaults. Finally, based on the available data and according to the results of the CHAID decision tree, contract maturity, amount of interest, number of installments, operating profit to asset, type of contract, average debt  in 3 months ago and loan amount are the most important indicators affecting the customers’ behavior. Taking these indicators into account, before granting the loans, could have a significant role in the prediction of the customers’ behavior and the related decision making.
 

Despite the consensus on the importance of nominal rigidities, there is no general agreement among monetary economists regarding the most appropriate and consistent pricing model that must be used to assess the effects of monetary policies in the economy. Due to the lack of empirical evidence with relation to the pricing behavior of Iranian firms, there is no general agreement on how to introduce nominal rigidities in monetary macro models (New Keynesian). In this case, choosing a pricing model is entirely arbitrary and based on the researcher’s judgment. In the absence of an accurate understanding of the role of different pricing models in analyzing the impacts of monetary policy, we face serious challenges in policy assessment. In order to fill this gap, we compare sticky information (Mankiw and Reis, 2002), dual stickiness (Dupor et al. , 2010), and Hybrid using dynamic stochastic general equilibrium (DSGE) approach. In this study, four criteria used to compare pricing models: 1) Posterior model probability comparison, 2) comparing simulated moments with those of actual data, 3) autocorrelation of inflation and 4) impulse response function. Based on the result, dual-stickiness Phillips curve (Simultaneous price and information stickiness) can match the stylized facts of Iran’s economy in comparison to other pricing models.