Banking
Mohammad Ali Dehghan Dehnavi; Meysam Amiri; Amin Khorshidsavar
Abstract
Banks play a crucial role in maintaining financial stability within an economy. Their importance arises from the various functions they perform, which contribute to the overall stability and growth of the financial system. Additionally, the significance of banks for real economic growth lies in their ...
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Banks play a crucial role in maintaining financial stability within an economy. Their importance arises from the various functions they perform, which contribute to the overall stability and growth of the financial system. Additionally, the significance of banks for real economic growth lies in their role as financial intermediaries that facilitate allocating capital efficiently, supporting businesses and individuals, and contributing to the economy’s overall stability and development. Relying on data from 16 banks in Iran between 2011 and 2023, this research aimed to investigate the factors influencing bank risk-taking, with a focus on monetary policy, regulations, and macroeconomic variables. The analysis used two models and the Generalized Method of Moments (GMM) as the estimation method. The results of the research show that there is an inverse relationship between monetary policy and risk-taking. The results indicated an inverse relationship between monetary policy and risk-taking. Moreover, while the capital adequacy ratio (a regulatory factor) and GDP growth rate positively influence risk-taking, there is an inverse relationship between the inflation rate and risk-taking.IntroductionBorio and Zhu (2012) introduced a new transmission mechanism of monetary policy known as the risk-taking channel. Building on the seminal work of Borio and Zhu (2012), numerous theoretical and empirical studies have validated and expanded this channel in various countries, including China (Li & Tian, 2020; Tan & Li, 2016). In general, the risk-taking channel is underpinned by three key mechanisms: search-for-yield; valuation, income, and cash flow expansion; and central bank communication, announcements, and feedback (Altunbas et al., 2012; Borio & Zhu, 2012; De Nicolò et al., 2010). Since interest rates influence banks’ risk-taking behavior through agency problems (Altunbas et al., 2012), other bank characteristics must also influence bank risk-taking via the same channel (Bonfim & Soares, 2018). Altunbas et al. (2011) note that banks with less capital, more assets, and a greater reliance on short-term market funding are exposed to higher risk. In addition, most studies have explore this theme from the perspective of internal bank characteristics, such as capital, liquidity, leverage, and the proportion of traditional business (Altunbas et al., 2012; Bonfim & Soares, 2018).Traditional moral hazard theory suggests that under-capitalized banks face significant agency problems and are more likely to take excessive risks (Jiang et al., 2020). Shim (2013) demonstrates that a capital buffer (i.e., capital above the required minimum) helps limit moral hazard and absorbs adverse economic shocks. During the early stages of a financial crisis, banks with higher Tier I capital and more liquid assets tend to perform better (Beltratti and Stulz, 2009; Demirgüç-Kunt et al., 2013). Some empirical studies of the U.S. banking system suggest that capital is an effective risk indicator, showing a significant negative correlation with bank risk-taking (Hogan, 2015). Overall, it is widely accepted among scholars that holding more capital reduces bank risk-taking.The findings of Gizki et al. (2001) support the relationship between financial institutions and the real economy. First, the effect of real credit growth on banks’ credit risk and profitability aligns with the view that challenges in monitoring bank performance can lead to weakened credit standards during periods of rapid aggregate credit expansion.Second, the observed relationship between property prices and bank risk supports the proposition that difficulties in monitoring borrowers’ viability—coupled with the effect of collateral values on signaling borrower creditworthiness—play a crucial role in determining credit supply. Third, the results are consistent with theoretical analyses suggesting that cyclical changes in agents’ preferences for leverage significantly influence bank risk and profitability.Materials and MethodsA key assumption of regression analysis is that the right-hand side variables are not correlated with the disturbance term. If this assumption is violated, both ordinary least squares (OLS) and weighted least squares (WLS) estimations become biased and inconsistent. There are several situations in which some of the right-hand side variables may be correlated with the disturbance term. Classic examples of such cases include: 1) There are endogenously determined variables on the right-hand side of the equation, and 2) these right-hand side variables are measured with error. For simplicity, we refer to variables that are correlated with the residuals as endogenous, and those that are not correlated with the residuals as exogenous or predetermined. The standard approach when right-hand side variables are correlated with the residuals is to estimate the equation using instrumental variables regression. The concept behind instrumental variables is to identify a set of variables, called instruments, that are both correlated with the explanatory variables in the equation and uncorrelated with the disturbances. These instruments are then used to remove the correlation between the right-hand side variables and the disturbances. There are several approaches to using instruments to eliminate the effect of variable and residual correlation. The current study proposed instrumental variable estimators that employ the Generalized Method of Moments (GMM).Results and DiscussionThe GMM was used to estimate the model. The results are shown in Table 1.Table 1. Model estimation resultsModel 2Model 1SymbolVariable-0/07***RWAlag dependent variable0/57***-NPLlag dependent variable-0/33***-1/48***OvernightInterbank interest rate0/16***0/48***CARCapital adequacy ratio0/07**0/25***GDP_GrowthGross domestic product growth-0/02**-0/35***inflationInflation rate-0/13***-0/03SizeSize0/000**0/00*LeverageLeverage0.990/99SarganSargan-0/13**-1/99**AR(1)First-Order Autoregressive-0/06-1/67AR(2)Second-Order Autoregressive* The coefficient is significant at 10% level.** The coefficient is significant at 5% level.*** The coefficient is significant at 1% level.Source: Research calculations.ConclusionThe role of the financial system in the economy, along with its development and health, forms the foundation for strengthening and driving economic growth. Monitoring and reforming this system contribute to stability by addressing needs and reinforcing the real sector of the economy. The research findings indicated an inverse relationship between monetary policy and risk-taking. While the capital adequacy ratio (a regulatory factor) and GDP growth rate have a positive effect on risk-taking, there is an inverse relationship between the inflation rate and risk-taking.
Financial Economics
Hamid Reza Arbab; Hamid Amadeh; Amin Amini
Abstract
This study investigated the factors that leads to economic uncertainty which may influence the petrochemical companies returns in various market conditions regarding their various levels of capital. To meet this object, we used quarterly data on government’s current expenditures, general government ...
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This study investigated the factors that leads to economic uncertainty which may influence the petrochemical companies returns in various market conditions regarding their various levels of capital. To meet this object, we used quarterly data on government’s current expenditures, general government revenues, liquidity, GDP, and exchange rate, as the political variables for the years 1384-1397. Considering the type of available time series, we exercised the ARIMA-GARCH model to create an indicator to show the uncertainty of economic policies. We used the result to estimate the quantile regression model, along with other factors affecting corporate returns, including the price of the OPEC oil basket and the real rate of returns and market exchange rate. The results of this study indicated that in the bearish market, the greatest negative effect of each economic policy uncertainty is on the companies with lesser capital. Moreover, the intensity of this effect decreases as the market tends to change from bearish to bullish, and finally the economic policy uncertainty will have the least impact on companies with bigger capital.
Macroeconomics
Mohammad Ali Aboutorabi; Mehdi Hajamini; Sahar Tohidi
Abstract
In recent decades, the effect of financial development on real sector growth has been discussed from different aspects. This paper focuses on financial structure and explains the role of bank-based and market-based financial structures on economic growth by classifying the literature. Using the FMOLS ...
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In recent decades, the effect of financial development on real sector growth has been discussed from different aspects. This paper focuses on financial structure and explains the role of bank-based and market-based financial structures on economic growth by classifying the literature. Using the FMOLS method for the period 1979-2016, the effects of financial structure and banking structure on per capita GDP and sectors’ growth (agriculture, industry, and services) in Iran are estimated. Empirical findings indicate that discriminating policies and bias in financial structure in favor of a specific sector has a negative effect on real sector growth, especially agriculture and industry. Therefore, in support of the design of a balanced financial structure, it is recommended that the state should avoid any intervention or discrimination in favor of a specific sector. In the case of banking structure, the findings show that increasing the financial strength of banks encourages economic growth.
Reza Yousefi Hajiabad; Zohreh Hooshmand; Maryam Khoshnevis
Abstract
The main purpose of this paper is to investigate the interaction effects of risk, capitalization and inefficiency in Iran's banking system. For this purpose, combined data of commercial and private banks of Iran in years (1999-2012), were collected and analyzed using simultaneous equation approach and ...
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The main purpose of this paper is to investigate the interaction effects of risk, capitalization and inefficiency in Iran's banking system. For this purpose, combined data of commercial and private banks of Iran in years (1999-2012), were collected and analyzed using simultaneous equation approach and fixed effects two-stage least squares (FE2SLS), The results confirm the belief that these three variables are simultaneously determined. The results indicate that relationship between inefficiency with quality of loans is significant and positive. Capitalization and loan growth have positive effect on inefficiency. Capital accumulation will decrease quality loans. Capital accumulation also has negative effects on the quality of banking risk indicator.On the other hand ,due to the inefficiency of the banking system's cost and return on assets on capital accumulation, banks that aren't in a good position in terms of performance, are not in a right position in terms of equipping and capital accumulation either.
Mehdi Sadeghi; Seyyed Rohollah Ahmadi
Volume 17, Issue 51 , July 2012, , Pages 89-112
Abstract
Policy-makers around the world have emphasized the virtues of deregulation. The banking industry in Iran has partly experienced reform after the domestic peivate banks entry since 2000. This study investigates the impact of this policy on economic efficiency of banking sector by using banking data over ...
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Policy-makers around the world have emphasized the virtues of deregulation. The banking industry in Iran has partly experienced reform after the domestic peivate banks entry since 2000. This study investigates the impact of this policy on economic efficiency of banking sector by using banking data over the period 1997-2006. We measured the economic efficiency of banks using SDEA which necessitated a grasp of technical and allocation efficiency and calculated the model by GAMS software. The purpose of stochastic setting of DEA is accommodating both the inefficiency and the presence of measurement errors. In the second step we used the fixed effect model on panel data to regress efficiency measures on policy variable of entry. We solved the regression model by Eviews and Stata softwares. Our result show that entry variable has not meaningful effect on efficiency. So economic efficiency cannot be differentiated on the basis of policy reform of entry.
Hamid Kordbacheh; Leila Pordel Nooshabadi
Volume 16, Issue 49 , February 2012, , Pages 117-150
Abstract
This paper estimates a dynamic panel model to examine the bank-specific and macroeconomic determinants of non-performing loans in Iranian banking sector using a sample of 12 banks over the period of 2002-2008. The findings of this paper show that prudential behaviour, size and ownership status of banks ...
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This paper estimates a dynamic panel model to examine the bank-specific and macroeconomic determinants of non-performing loans in Iranian banking sector using a sample of 12 banks over the period of 2002-2008. The findings of this paper show that prudential behaviour, size and ownership status of banks are the main statistically significant bank-specific factors of non-performing loans. For robustness of the empirical results, the model has been estimated with alternative indexes of business cycle variable. The findings of the estimated models show that the macroeconomic conditions have significant impact on the non-performing loans in the sample.
