تأثیر نااطمینانی اقتصادی و ریسک اعتباری بر وام دهی بانک های پذیرفته شده در بورس اوراق بهادار تهران با استفاده از روش گشتاورهای تعمیم یافته (GMM)

محورهای موضوعی : فصلنامه اقتصاد محاسباتی

هادی جان زاد پریجایی ¹ , غلامعلی حاجی ^{2 *} , احمد سرلک ³

1 - مدیر دانشجویی دانشگاه مازندران
2 - عضو هیات علمی دانشگاه آزاد اسلامی واحد اراک
3 - هیات علمی

تاریخ دریافت : 1403/10/04 تاریخ پذیرش : 1403/12/15 تاریخ انتشار : 1404/04/25

کلید واژه: نااطمینانی اقتصادی, ریسک اعتباری, وام دهی, بانک های پذیرفته شده در بورس اوراق بهادار تهران,

چکیده مقاله :

در اقتصاد امروز، بانک‌ها به عنوان پل ارتباطی میان بخش‌های واقعی و مالی، نقشی حیاتی ایفا می‌کنند. بانکها با سازماندهی مدیریت جریان‌های مالی، تسهیل مبادلات را ممکن می‌سازند و به توسعه بازارها و رونق اقتصاد کمک می‌کنند. با این حال، عوامل مختلفی بر عملکرد بانک‌ها در زمینه اعطای وام تأثیرگذار است. این مطالعه به بررسی تأثیر دو عامل کلیدی، یعنی نااطمینانی اقتصادی و ریسک اعتباری، بر تصمیمات وام‌دهی بانک‌های منتخب در بورس تهران می‌پردازد. در این پژوهش، داده‌های ۱۰ بانک در بازه زمانی ۱۳۹۲ تا ۱۴۰۱ مورد تحلیل قرار گرفت. با بکارگیری روش گشتاورهای تعمیم یافته برآورد مدل  نشان می دهد که نااطمینانی اقتصادی تأثیر قابل توجهی بر رفتار وام‌دهی بانک‌ها ندارد؛ در حالی که ریسک اعتباری به طور معناداری باعث کاهش تمایل بانک‌ها به اعطای وام شده است. این یافته‌ها حاکی از آن است که بانک‌ها در رویارویی با ریسک اعتباری، رویکرد محتاطانه‌تری در وام‌دهی اتخاذ می‌کنند.

 

چکیده انگلیسی:

Extended Abstract

Purpose

Today, banks play an important role in creating a connection between the real and monetary sectors of the economy, and by organizing receipts and payments and providing exchange facilities, they facilitate the expansion of markets, growth and prosperity of the economy. Economic uncertainty and credit risk are among the most important variables that can have a great impact on lending by banks and credit institutions. The purpose of this research is to investigate the effect of economic uncertainty and credit risk on the lending decisions of banks admitted to the Tehran Stock Exchange. The statistical sample of the research includes 10 public and private banks admitted to the Tehran Stock Exchange during the period from 2013 to 2022. The data were analyzed using unit root, kao and generalized method of moments (GMM) tests with the help of Eviews software. The results of the research showed that economic uncertainty had no significant effect on the lending behavior of banks and credit risk had a negative and significant effect on the lending behavior of banks. Economic uncertainty refers to uncertainty about future economic events. Early work by Knight (1921) described the consequences of uncertainty, and following the Great Depression, increased attention to the potentially harmful consequences of high uncertainty. Economic uncertainty led to the expansion of researchers' attention and efforts to investigate the importance of uncertainty and escape from the ambiguities caused by it(moradi, et.al, 2022). Due to the importance and effectiveness of economic uncertainty, extensive studies have focused on its measurement. A group of researchers have focused on the conditional fluctuations of variables as a substitute for uncertainty indicators, which in this group's approach, focus only on individual uncertainties of macroeconomic variables, such as uncertainty of inflation, exchange rate, interest rate, monetary growth and stock index.(Binder, 2017). This research examines the effect of economic uncertainty and credit risk on the lending decisions of banks based on the information of banks admitted to Tehran Stock Exchange and raises the question that economic uncertainty and What effect does credit risk have on the lending decisions of banks admitted to Tehran Stock Exchange?

Methodology

According to the research literature section and the purpose of the research on the effect of economic uncertainty and credit risk on bank lending, the following regression model was used.

DLog (BL) I, (t,t+1)=β0 + β1EU+  β2CR,it + β3ROEit + β4MBit + β5SIZEit + εit

Dependent variable:

Logarithm of change in bank lending (BL): This variable is obtained from the difference between loans at time t and t+1. It is expected that the bank's lending behavior will change from time to time and from one bank to another according to the characteristic factors of the bank as well as macroeconomic factors.

Independent variable:

Economic uncertainty (EU): First, macroeconomic uncertainty vectors using the generalized conditional heterogeneous variance model (GARCH (-,-)) based on the equation of mean and conditional variance (separately for each variable), in the form of below equations are extracted;

                                                                                                            

Credit Risk (CR): Credit risk is the risk that arises from the default of the counterparty, or more generally, the risk that arises from a "credit event"(Gorton & Metrick, 2012).

Finding

In this study, Levin, Lin and Chu test, generalized Fisher- Dickey–Fuller test and Fisher-Phillips-Peron test were used to check the significance of the variables.

The results and examination of the calculated statistics values and their acceptance probability show that all the variables except economic uncertainty are at the level of significance and the variable of economic uncertainty is significant with one-time differentiation.

Based on the results of the Sargan Test, the null hypothesis that the residuals are correlated with the instrumental variables is rejected, therefore the instrumental variables used in the estimation of the model have the necessary validity; In other words, the results of the Sargan Test show that there is no relationship between the error components and the tools used in the estimation of the research model, and as a result, the validity of the results for interpretation is confirmed.

The results showed that the concentration of credit risk has a negative effect on the lending behavior of banks in such a way that with an increase of one unit in credit risk; Bank lending will decrease by 0.28% and considering that the significance level of the estimated coefficient is less than 0.05%; Therefore, the influence of credit risk on the lending behavior of banks is statistically significant.

Conclusion

The results of the research showed that economic uncertainty had no significant effect on the lending behavior of banks, and credit risk had a negative and significant effect on the lending behavior of banks. The absence of a significant relationship between economic uncertainty and the lending behavior of banks can be related to the conventional mechanism of the country's banking system in the granting of facilities. The origin of the major part of banking facilities is related to the legal requirements and the mandatory structure of lending in Iran. According to the mentioned issue, economic instability cannot have a noticeable effect on the amount of lending by banks. Considering the significant relationship between credit risk and the lending behavior of banks, reduction and control of credit risk has been proposed as one of the effective factors in improving the process of granting credit and as a result in the performance of banks, and the main role in continuing to provide facilities, profitability and the survival of banks and financial institutions.

منابع و مأخذ:

References
-Alaei, R., Salahmanesh, A. and Armen, S. A. (2019). Determination of optimal economic uncertainty index for Iranian economy. Economic Strategy, 8(28),111-145. (In Persian)
-Alessandri, P. and Bottero, M. (2020). Bank lending in uncertain times. European Economic Review. 128(2):103503. Retrieved from https://doi.org/10.1016/j.euroecorev.2020.103503/
-Arbab, H. R., Amadeh, H. and Amini, A. (2021). The Impact of Economic Policy Uncertainty on the Returns of Petrochemical Companies in Different Market Conditions. Iranian Journal of Economic Research, 26(88), 191-221. Retrieved from https://doi.org/ 10.22054/ijer.2021.50187.838/ (In Persian).
-Arelano, M. and Bond, S. (1991). Some tests of specification in panel data: Monte Carlo evidence and an application to employment equations. Review of Economics and Statistics, 58(2), 277-297.
-Azarpandar, F. (2014). Investigating the relationship between liquidity risk and political risk in banks), Senior Master's thesis, Islamic Azad University, Science and Research Branch. (In Persian).
-Baker, S. R., Bloom, N. and Davis, S. J. (2016). Measuring economic policy uncertainty. The quarterly journal of economics, 131(4), 1593-1636.
-Bakhtiar M, Moayedfar R, Vaez Barzani M. & Mojab R. (2023). Investigating the Three Dimensions of Credit Risk of Banks in Iran with an Emphasis on the Geographical Location of the Enterprise. The Economic Research, 23 (1), 221-247.
Retrieved from https://dor.isc.ac/ 20.1001.1.17356768.1402.23.1.9.1/ (In Persian).
-Bernanke, B. S. and Blinder, A. S. (1992). The federal funds rate and the channels of monetary transmission. The American Economic Review,82(4), 901-921. Retrieved from https://www.jstor.org/stable/2117350/
-Binder, C. C. (2017). Measuring Uncertainty Based on Rounding: New Method and Application to Inflation Expectations. Journal Of Monetary Economics, 90(c), 1–12.
Retrieved from https://doi.org/10.1016/j.jmoneco.2017.06.001/
-Bond, R. (2002). Dynamic panel data model: A guide to micro data methods and practice. The Institute for Fiscal Studies Department of Economics, 1-34.
-Carvallo, O. & Pagliacci, C. (2018). Macroeconomic shocks, bank stability and the housing market in Venezuela. Emerging Markets Review,26 (C), 174-196. Retrieved from https://doi.org/ 10.1016/j.ememar.2015.12.002/
-Chi, Q. and Li, W. (2017). Economic policy uncertainty, credit risks and banks’ lending decisions: evidence from Chinese commercial banks. China Journal of Accounting Research,10(1),33-50. Retrieved from http://dx.doi.org/10.1016/j.cjar.2016.12.001/
-Christopher, S. & Bamidele, I. (2009). The impact of macroeconomic instability on the banking sector lending behavior in Nigeria. Journal of Money, Investment and Banking, 7, 88-100.
-Erdem, H.F. and Yamak, R. (2016). Measuring The Optimal Macroeconomic Uncertainty Index for Turkey. Economic Annals, 61(210), 7–22. Retrieved from http://dx.doi.org/10.2298/EKA1610007E/
-Flamini, V., McDonald A. and Schumacher, B.L. (2009). The determinants of commercial bank profitability in Sub-Saharan Africa. IMF Working Paper,09/15.
-Gan, P.T. (2014). The Optimal Economic Uncertainty Index: A Grid Search Application. Computational Economics, 43(2), 159–182. Retrieved from https://doi.org/10.1007/s10614-013-9366-y/
-Goodhart, C.A.E. (1984). Problems of Monetary Management: The UK Experience. In: Monetary Theory and Practice. Palgrave, London,91-121. Retrieved from https://doi.org/10.1007/978-1-349-17295-5_4
-Heidari, H., sadeghpour, S. and dehghandorost, M. (2017). The Relationship between Inflation Uncertainty and the Bank Loan Facilities Granted. Monetary & Financial Economics, 24(13), 135-154. Retrieved from https://doi.org/ 10.22067/pm. v24i14.49447/ (In Persian)
-Jafari Nodoushan, A., Mousavi, S. S. and Teymoorian, H. (2025). Investigating the effect of credit risk and liquidity risk on the efficiency of banks with dynamic data envelopment analysis. Journal of Investment Knowledge, 14(53), 51-67. Retrieved from https://doi.org/ 10.30495/jik.0621.23458/ (In Persian)
-Jafari Samimi, A., Azami, K. and Azizian, J. (2015). The effect of macroeconomics variables uncertainty on import of selected developing countries. Quarterly Journal of Quantitative Economics (JQE), 12(3), 27-49. Retrieved from https://doi.org/ 10.22055/jqe.2015.11892/ (In Persian)
-Jia, C. (2009). The effect of ownership on the prudential behavior of banks–the case of china. Journal of Banking & Finance, 33(1), 77-87.
-Jimenez, G. and Saurina, J. (2006). Credit Cycles, Credit Risk, and Prudential Regulation. International Journal of Central Banking, 2(2), 65 -98.
-Juelsrud, R. E. and Larsen, V.H. (2023). Macroeconomic uncertainty and bank lending. Economics Letters, Elsevier, 225(C). Retrieved from https://doi.org/ 10.1016/j.econlet.2023.111041/
-Knight, F. H. (1921). Uncertainty and Profit (first publ). London: London School of Economics
-Malkesh, E., Mehregan, N., erfani, A. and Abounoori, E. (2021). Determining the Heterogeneity of Banks Lending Behavior in Response to Monetary Policy. The Journal of Economic Studies and Policies, 8(1), 201-223. Retrieved from https://doi.org/10.22096/esp.2021.130956.1375/(In Persian)
-Mbutor, M. O. (2010). Exchange rate volatility, stock price fluctuations and the lending behaviour of banks in Nigeria. Journal of Economics and International Finance, 2(11), 251-260.
Retrieved from https://doi.org/10.5897/JEIF.9000047/
-Mirzaei, H., Falihi, N. and Mashhadian Maleki, R. (2012). The effect of uncertainty of macroeconomic variables (exchange rate and inflation) on the credit risk of legal customers of Tejarat Bank. Financial Economics, 6(18), 113-137.
Retrieved from https://dor.isc.ac/ 20.1001.1.25383833.1391.6.18.6.2/ (In Persian)
-Moore, A. (2017). Measuring Economic Uncertainty and Its Effects. Economic Record, 93(303), 550–575. Retrieved from https://doi.org/ 10.1111/1475-4932.12356/
-Moradi, F., Agheli L. and Asari Arani, A. (2022). The impact of uncertainty in economic policies on energy intensity in Iran. Quarterly Energy Economics Review, 18 (72) ,27-58. (In Persian)
-Olawale, S. L. (2017). The Effect of Credit Risk on the Performance of Commercial Banks in Nigeria.
Retrieved from http://dx.doi.org/10.2139/ssrn.2536531/
-Pedram, M., Kurdbacheh, H. and Moftakhari Badieenejad, T. (2016). The Effect of Macroeconomic Uncertainty on Banks' Lending in Iran. Iranian Economic Development Analyses, 4(4), 67-90. Retrieved from https:// doi.org/10.22051/edp.2018.14245.1079/ (In Persian)
-Rahimi Baghi, A., ArabSalehi, M. and Vaez Barzani, M. (2019). Assessing the Systemic Risk in the Financial System of Iran using Granger Causality Network Method. Financial Research Journal, 21(1), 121-142. Retrieved from https:// doi.org/ 10.22059/frj.2019.260749.1006682/ (In Persian).
-Rezaei, N, and Norouzi, A. (2019). Economic policy uncertainty, banks’ lending decisions. Journal of Investment Knowledge, 8(32), 315-330. (In Persian)
-Shahmohammadi, F., Kiani, A., Barzani., M. V., and Rabbani, H. (2015). Studying the impact of credit risk and liquidity risk on the health of the Iranian banking system, National Conference on Organizational Risk Management, Tehran, Narkish Information Institute. (In Persian)
-Sheikh Ali, V. (2019). Investigating the effect of liquidity risk and political risk on the level of banking stability index and banking performance in Mellat Bank, Second International Conference on Management, Industrial Engineering, Economics and Accounting, Tbilisi-Georgia, Permanent Secretariat in cooperation with Imam Sadeq University (AS). (In Persian)
-Sheri Anaghiz, S., Assadi, G. H. A. and Nikravesh, M. (2019). New Managerial Overconfidence Assessment Model and Earnings Forecasts: Generalized Method of Moments (GMM). Empirical Studies in Financial Accounting, 16(62), 1-20.
Retrieved from http://dx.doi.org/ 10.22054/qjma.2019.10411/ (In Persian)
-Talavera, O. Tsapin, A. and Zholud, O. (2019). Macroeconomic uncertainty and bank lending: the case of Ukraine. Economic Systems, 36(2), 279-293. Retrieved from https://doi.org/ 10.1016/j.ecosys.2011.06.005/
-Taghinezhadomran, V., Elmi, Z. M. and Husseinpor, F. Z. (2021). The Impact of Business Cycle on Bank Leverage Determinants. Iranian Journal of Economic Research, 26(88), 129-156. Retrieved from https://doi.org/ 10.22054/ijer.2021.58154.936/ (In Persian)
-Wu, W.S. and Suardi, S. (2021). Economic Uncertainty and Bank Lending. Journal of Money, credit and Banking. 53(8), 2037-2069. Retrieved from https://doi.org/ 10.1111/jmcb.12779/
-Zhang, X. Guo, D. Xiao, Y. and Wang, M. (2017). “Do Spatial Spillover Effects of Non -performing Loans for Commercial Banks Exist? Evidence from Chinese Provinces. Emerging Markets Finance and Trade, 53(9), 2039 -2051. Retrieved from https://doi.org/10.1080/1540496X.2017.1280668/