عوامل مؤثر بر ریسک پذیری بانک ها با تأکید بر سیاست پولی الزامات مقرراتی و اقتصاد کلان (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
بانک ها نقش مهمی در حفظ ثبات مالی در یک اقتصاد دارند. اهمیت آنها ناشی از کارکردها و نقش هایی است که انجام می دهند و به ثبات کلی و رشد سیستم مالی کمک می کنند. از طرف دیگر اهمیت بانک ها برای رشد اقتصاد واقعی در نقش آنها به عنوان واسطه های مالی نهفته است که تخصیص کارآمد سرمایه، حمایت از مشاغل و افراد را تسهیل می کند. یک بخش بانکی سالم و تنظیم شده در ارتقای رشد پایدار اقتصادی مؤثر است. این پژوهش به بررسی عوامل مؤثر بر ریسک پذیری بانک ها با تأکید بر سیاست پولی، الزامات مقرراتی و اقتصاد کلان در 16 بانک ایرانی طی سال های 1390 تا 1402 پرداخته است. در این مطالعه از دو مدل استفاده شده و روش تخمین ضرایب GMM می باشد. نتایج پژوهش نشان داده که بین سیاست پولی انبساطی با ریسک پذیری رابطه معکوس وجود دارد. درحالی که کفایت سرمایه (الزمات مقرراتی) و نرخ رشد تولید ناخالص داخلی اثر مثبت روی ریسک پذیری دارد، رابطه بین نرخ تورم و ریسک پذیری معکوس می باشد.Factors Influencing Bank Risk-Taking With an Emphasis on Monetary Policy, Regulations, and Macroeconomics
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. Introduction Borio 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 Methods A 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 Discussion The GMM was used to estimate the model. The results are shown in Table 1. Table 1. Model estimation results Model 2 Model 1 Symbol Variable - 0/07*** RWA lag dependent variable 0/57*** - NPL lag dependent variable -0/33*** -1/48*** Overnight Interbank interest rate 0/16*** 0/48*** CAR Capital adequacy ratio 0/07** 0/25*** GDP_Growth Gross domestic product growth -0/02** -0/35*** inflation Inflation rate -0/13*** -0/03 Size Size 0/000** 0/00* Leverage Leverage 0.99 0/99 Sargan Sargan -0/13** -1/99** AR(1) First-Order Autoregressive -0/06 -1/67 AR(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. Conclusion The 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.
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