تأثیرپذیری ریسک سیستماتیک شبکه بانکی کشور از ریسک اعتباری : شواهد تجربی جدید از روش چند عاملی بانک محور پویای تصادفی (DBMM)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 مدرس مدعو، گروه علوم اقتصادی، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی

2 استاد اقتصاد، گروه اقتصاد نظری، دانشکده علوم اقتصادی و اداری، دانشگاه مازندران

چکیده

در این مقاله تأثیر ریسک اعتباری بر ریسک سیستماتیک شبکه بانکی کشور به روش چند عاملی بانک محور پویای تصادفی (DBMM) بررسی شده است. عامل­های بازی پویای تصادفی در این تحقیق شامل بانک­ها، بانک مرکزی، سپرده­گذاران و بنگاه­ها، و قلمرو زمانی داده­ها متعلق به بازه سال­های 1402- 1397 هستند. بر اساس نتایج بدست آمده،کاهش سهم مطالبات غیرجاری، ریسک نقدینگی، سرایت سیستماتیک و ریسک سیستماتیک شبکه بانکی را کاهش داده و به ثبات سیستم بانکی کمک می­نماید. همچنین، این پژوهش نشان می­دهد که کاهش سهم مطالبات غیرجاری، نقدینگی سیستم بانکی و میزان سرمایه بانک­ها را افزایش داده و نوسانات تخصیص اعتبار به بخش واقعی اقتصاد را کاهش می­دهد. کنترل مطالبات غیر جاری با هدف و کنترل ریسک سیستماتیک و افزایش ثبات در شبکه بانکی کشور، از جمله توصیه­های سیاستی پژوهش حاضر محسوب می­شود.  

کلیدواژه‌ها

عنوان مقاله [English]

The influence of the systematic risk of the country's banking network on credit risk: new empirical evidence from the random dynamic bank-oriented multi-agent model (DBMM).

نویسندگان [English]

  • Roghayeh Nazemfar 1
  • Amir Mansour Tehranchian 2
1 Faculty of Social Sciences, University of Mohaghegh Ardabili
2 Professor in Economics, Department of Theoretical Economics, Faculty of Economic and Administrative Sciences, University of Mazandaran
چکیده [English]

In this article, the effect of credit risk on the systematic risk of the country's banking network has been investigated using the stochastic dynamic bank-oriented multi-agent-based method (DBMM). Random dynamic game agents include banks, central bank, depositors and firms, and the time domain of the data belongs to the period of 2018-2023. According to the obtained results, reducing the share of non-current claims, reduced liquidity risk, systematic contagion and systematic risk of the banking network, and helps the stability of the banking system. Also, this research shows that reducing the share of non-current claims increases the liquidity of the banking system and the amount of banks' capital and reduces the fluctuations of credit allocation to the real sector of the economy. The control of non-current claims with the aim of controlling systematic risk and increasing stability in the country's banking network is one of the policy recommendations of this research.

کلیدواژه‌ها [English]

  • dynamic bank-oriented multi-agent model
  • credit risk. systemic risk

مراجع

Alimohammadi, M., Fallah Shams, M., & ebadi, K. (2021). Designing of a early warning system for credit risk in the banking system. Quarterly Studies in Banking Management and Islamic Banking, 7(17), 67-90. (In Persian)
Barati, L., Falah Shams, M., Ghafari, F., & Heidarzadeh Hanzaei, A. (2023). Measuring systemic risk and the effect of fundamental variables on it in the country's banking system. Journal of Investment Knowledge, 12(48), 721-744. (In Persian)
Bardoscia, M., Battiston, S., Caccioli, F., & Caldarelli, G. (2017). Pathways towards instability in financial networks. Nature communications, 8(1), 14416.
Bargigli, L., Riccetti, L., Russo, A., & Gallegati, M. (2020). Network calibration and metamodeling of a financial accelerator agent based model. Journal of Economic Interaction and Coordination, 15(2), 413-440.
Barroso, R. V., Lima, J. I. A. V., Lucchetti, A. H., & Cajueiro, D. O. (2016). Interbank network and regulation policies: an analysis through agent-based simulations with adaptive learning.
Berloco, C., De Francisci Morales, G., Frassineti, D., Greco, G., Kumarasinghe, H., Lamieri, M., ... & Yang, S. (2021). Predicting corporate credit risk: Network contagion via trade credit. PLoS One, 16(4), e0250115.
Camerer C. F. and T.H. Ho (1999). Experience weighted attraction learning in normal form games. Econometrica, 67, 827–874.
Chen, T., & Wang, S. (2023). Incomplete information model of credit default of micro and small enterprises. International Journal of Finance & Economics, 28(3), 2956-2974.
Chen, T., He, J., & Li, X. (2017). An evolving network model of credit risk contagion in the financial market. Technological and Economic Development of Economy, 23(1), 22-37.
Chen, T., Yang, Q., Wang, Y., & Wang, S. (2020). Double‐Layer Network Model of Bank‐Enterprise Counterparty Credit Risk Contagion. Complexity, 2020(1), 3690848.
Chen, T., Wang, Y., Zeng, Q., & Luo, J. (2020). Network model of credit risk contagion in the interbank market by considering bank runs and the fire sale of external assets. Physica A: Statistical Mechanics and Its Applications, 542, 123006.
Dolfin, M., Knopoff, D., Limosani, M., & Xibilia, M. G. (2019). Credit risk contagion and systemic risk on networks. Mathematics, 7(8), 713.
 
Fadaee vahed, M., Dehghan Dehnavi, M. A., divandari, A., & amiry, M. (2020). Investigating the effect of risk and competitiveness indicators of banks on systemic risk with the marginal expected shortfall (MES) approach using the GMM model. Journal of Investment Knowledge, 9(36), 317-334. (In Persian)
Fallah Shams, M., & Banisharif, A. (2021). Investigating the Financial Risk Spillover in Banks Accepted in Tehran Stock Exchange Market through MGARCH Approach. Financial Research Journal, 23(1), 87-107. (In Persian)
Gudarzi Farahani, Y., Morsaliarzanagh, Z., Abbasi, E., & Mehrara, M. (2024). The effect of institutional variables on financial stability banking system. Public Sector Economics Studies, 3(3), 385-406. (In Persian)
Leduc, M. V., Poledna, S., & Thurner, S. (2017). Systemic risk management in financial networks with credit default swaps. Available at SSRN 2713200.‏
Li, S. W., Wang, L., Liu, X. X., & Zhang, J. (2020). Systemic risk across the banking sector and the firm sector. Sys. Eng. Theor. Pract, 10, 2492-2504.
Liu, X. (2023). A model of systemic bank runs. The Journal of Finance, 78(2), 731-793.
Mirza’i, E., Mohammadi, T., & Shakeri, A. (2016). The Interaction between Non-Performing Loans and Macroeconomic Conditions:A Panel Vector Autoregressive Approach. Economics Research, 16(60), 183-220. (In Persian)
Naili, M., & Lahrichi, Y. (2022). The determinants of banks' credit risk: Review of the literature and future research agenda. International Journal of Finance & Economics, 27(1), 334-360.
Sadeghi, Somayeh (2023). The Effect of Liquidity Creation on the Systemic Risk : by Concentration on Banks Balance Sheet Structure. Economic Modeling, 16(60), 69-86. (In Persian)
Xie, X., Shi, X., Gu, J., & Xu, X. (2023). Examining the contagion effect of credit risk in a supply chain under trade credit and bank loan offering. Omega, 115, 102751.
Zhang, M., He, J., & Li, S. (2018). Interbank lending, network structure and default risk contagion. Physica A: Statistical Mechanics and Its Applications, 493, 203-209.

فایل ها

هم رسانی

ارجاع به این مقاله

آمار