Systemic risk assessment of the banking system by modeling of the topology of the interbank market network

Subject Areas : Journal of Investment Knowledge

tayebeh zanganeh ¹ , Mohammad Ali Rastegar ² , kazem Chavoshi ³ , mirfeiz Fallah Shams ⁴

1 - Department of financial management, Science and Research branch, Islamic Azad University, Tehran, Iran
2 - Financial Engineering Group, Industrial and Systems Engineering Department, Tarbiat Modares University
3 - Assistant Prof., Department of Business Administration, Faculty of Management, Kharazmi University, Tehran, Iran
4 - Department management, Tehran Markaz branch, Islamic Azad University, Tehran, Iran

Received: 2019-10-17 Accepted : 2019-12-09 Published : 2020-11-21

Keywords: degree distribution, financial contagion, scale free networks, assortativity, Complex Network theory,

Abstract :

The objective of this paper is to analyze the network topology of the Iranian overnight money market through methods of statistical mechanics applied to complex networks in order to assessing systemic risk. We investigate differences in the activities of 33 Iranian banks dividing into different four types between 2010-2015 by analyzing 66 montly adjacency matrixs. Using degree distribution analysis of the networks, we find that that Iranian interbank market network is scale-free network and cumulative degree, in-degree and out-degree follows the power-law distribution. In terms of the criterion of assortativity, the interbank market network of Iran is assortative and core-periphery with one or more banks as the money center. The results show that the Iranian interbank network is vulnerable to shocks and has high level of systemic risk. Also, in the event of failure, the most vulnerable group is to privatized and specialist governmental banks, and the private banks, due to the high volume of exchanges and net negative flows, can put a considerable systemic risk to the interbank market network.

References:

• آذری قره لر، آ.، رستگار، م.ع.، (1394). بررسی ریسک سیستمی شرکت بر شرکت در شرکت های بورس اوراق بهادار تهران، سومین کنفرانس مدیریت، اقتصاد، حسابداری .


• توکلی، م.، عبدالرحیمیان، م،. و رعیتی شوازی، علیرضا. ( 1395). برآورد اثر نرخ بهره بین بانکی بر عملکرد (سودآوری) بانکهای ایران. پایان نامه کارشناسی ارشد دانشکده علوم انسانی ، دانشگاه علم و هنر.


• توکلیان، ح. ( 1390). بازار بین بانکی ریالی و قابلیت معرفی یک ابزار جدید سیاستگذاری پولی. تازه های اقتصاد، شماره صد و سی و سه، 107-104.


• شاهچرا، م.، طاهری، م.، (1397)، تاثیرات الزامات نقدینگی بر سیاست گذاری بانک مرکزی در بازار بین بانکی ایران، بیشت و هشتمین کنفرانس سیاست های پولی و ارزی.


• دستورالعمل اجرایی عملیات بازار بین بانکی ریالی، مصوب 1383.


• رستگار، م.ع. کریمی.ن. (1395)، ریسک سیستمی در بخش بانکی،مجله مدلسازی ریسک و مهندسی مالی، دوره 1، شماره 1، ص. 1-19.


• حاجیان، م.ر، (1385).، بازار بین بانکی ایران، تهران، انتشارات پژوهشکده پولی و بانکی .


• درودیان، ح.، (1389)گزارشی از وضعیت بازار بی نبانکی در ایران ، مؤسسه ی مطالعات و تحقیقات مبین.


• شمسی، س.، موسویان، ع.، نیلی، ف.، طالبی، م.، صالح آبادی، ع.، (1395). طراحی بازار بین بانکی در نظام پولی و مالی ایران مبتنی بر فقه امامیه. رساله دکتری.


• Aldasoro, I., Gatti, D. D., & Faia, E. (2017). Bank networks: Contagion, systemic risk and prudential policy. Journal of Economic Behavior & Organization, 142, 164-188.


• Allen, F. and Gale, D. (2000). Financial contagion. Journal of Political Economy, 108:1–


• Acemoglu, D., Ozdaglar, A., and Tahbaz-Salehi, A. (2015). Systemic risk and stability in financial networks. American Economic Review, 105(2):564–608.


• Allen, F., Gale, D., (2000). Financial contagion, Journal of political economy 108 (1), 1–33.


• Albert, R., Jeong, H., Barabasi, A.L., (2000). Error and attack tolerance of complex networks. Nature 406,378.


• Barthelemy, M., Barrat, A., Pastor-Satorras, R., and Vespignani, A. (2005). Characteriza tion and modeling of weighted networks. Physica A, 346:34–43.


• Barucci, E., Impenna, C., Reno` , R., (2004). The Italian overnight market: microstructure effects, the martingale hypothesis and the payment system. Research in Banking and Finance 4, 321–36


• Boss, M., Elsinger, H., summer, M., Thurner S.,(2007). Network topology of the interbank market, Quantitative Finance 4 (6), 677–684.


• Boss, M., Elsinger, H., Summer, M., Thurner, S., (2008), The Network Topology of the Interbank Market, Physica A.


• Costa, F., Rodrigues, L., Travieso, F.A., and Boas, P. R. V. (2005). Characterization of complex networks: A survey of measurements. Advances in Physics, 56(1):167–242.   


• Castro Miranda, R. C., Stancato de Souza, S. R., Silva, T. C., and Tabak, B. M. (2014). Connectivity and systemic risk in the Brazilian national payments system. Journal of Complex Networks, 2(4):585–613.


• Craig, B., Von Peter,G., (2014). Interbank tiering and money center banks, Journal of Financial Intermediation 23 (3) 322–347


• Clauset, A., Shalizi, C.R., Newman, M.E., (2009). Power-law distributions in empirical data, SIAM review 51 (4). 661–703.


• Cajueiro, D.O, Tabak, B.M, (2008).The role of banks in the brazilian interbank market: Does bank type matter?, Physica A: Statistical Mechanics and its Applications 387 (27), 6825–6836.


• Di Gangi, D., Sardo, D., Macchiati, V., Minh, T. P., Pinotti, F., Ramadiah, A., ... & Cimini, G. (2018). Network Sensitivity of Systemic Risk. arXiv preprint arXiv:1805.04325.


• Erol, S., & Vohra, R. (2018). Network formation and systemic risk. Available at SSRN 2546310.


• Engel, J., Pagano, A., & Scherer, M. (2019). Reconstructing the topology of financial networks from degree distributions and reciprocity. Journal of Multivariate Analysis.


• Freixas, X., Parigi, B., and Rochet, J. (2000). Systemic risk, interbank relations, and liquidity provision by the Central Bank. Journal of Money, Credit and Banking 32(3):611-638.


• Georg, C.-P. (2013). The effect of the interbank network structure on contagion and common shocks. Journal of Banking and Finance, 37(7):2216–2228.


• González-Avella, J. C., de Quadros, V. H., & Iglesias, J. R. (2016). Network topology and interbank credit risk. Chaos, Solitons & Fractals, 88, 235-243.


• Iori, G., Jafarey, S., Padilla, F.,) 2006(. Systemic risk on the interbank market. Journal of Economic Behaviour and Organization 61 (4), 525–542.     


• Iori, G., Reno, R., De Masi, G. Caldarelli, G., (2007). Trading strategies in the Italian interbank market, Physica A: Statistical Mechanics and its Applications 376  467–479


• G., Masib, G., Precupc, O.V., Gabbid, G., Caldarellifa, G.,(2007). A network analysis of the Italian overnight money market, Journal of Economic Dynamics & Control.


• Iori, G., De Masi,G., Precup, O.V., Gabbi, G. Caldarelli, G., (2007). A network analysis of the italian overnight money market, Journal of Economic Dynamics and Control 32 (1) , 259–278.


• Iori, G., Reno, R., De Masi, G., Caldarelli, G., (2007). Trading strategies in the Italian interbank market, Physica A: Statistical Mechanics and its Applications 376, 467–479.


• Krause, S. M., Štefančić, H., Zlatić, V., & Caldarelli, G. (2019). Controlling systemic risk-network structures that minimize it and node properties to calculate it. arXiv preprint arXiv:1902.08483.


• Leventides, J., Loukaki, K., & Papavassiliou, V. G. (2019). Simulating financial contagion dynamics in random interbank networks. Journal of Economic Behavior & Organization, 158, 500-525.


• Lee, S. H. (2013). Systemic liquidity shortages and interbank network structures. Journal of Financial Stability, 9(1):1–12.


• Li, S. and He, J. (2012). The impact of bank activities on contagion risk in interbank networks. Advances in Complex Systems, 15:1250086.


• Nier, E., Yang, J., Yorulmazer, T., and Alentorn, A. (2007). Network models and financial stability. Journal of Economic Dynamics and Control, 31(6):2033–2060.


• Newman, M. (2010). Networks: An Introduction. Oxford University Press, Inc., New York, NY, USA.


• Newman, M. E. (2002). Assortative mixing in networks. Physical Review Letters, 89(20):208701.


• Newman, M. E. J. (2003a). Mixing patterns in networks. Physical Review E, 67(2):026126.


• Newman, M. E. J. (2003b). The structure and function of complex networks. SIAM Review, 45:167–256.


• Papadimitriou, T., Gogas, P., and Tabak, B. M. (2013). Complex networks and banking systems supervision. Physica A: Statistical Mechanics and its Applications, 392(19):4429–4434.


• Silva, T. C. and Zhao, L. (2012). Network-based high level data classification. IEEE Transactions on Neural Networks and Learning Systems, 23(6):954–970.


• Silva, T. C. and Zhao, L. (2015). High-level pattern-based classification via tourist walks in networks. Information Sciences, 294:109–126.


• Silva, T.C., Rubens Stancato de Souza, S., Tabak, B.M., (2015). Network Structure Analysis of the Brazilian Interbank Market, June.


• Shouwei Li , Jianmin He, Yaming Zhuang, (2010). A network model of the interbank market, Physica A 389, 5587–5593.


• Souma, W., Fujiwara, Y., Aoyama, H., (2003). Complex networks and economics, Physica A: Statistical Mechanics and its Applications 324 (1), 396–401.


• Soram¨aki, K., Bech, M.L., Arnold, J., Glass,R.J., Beyeler, W.E., (2007). The topology of interbank payment flows, Physica A: Statistical Mechanics and its Applications 379 (1). 317–333


• Tabak, B. M., Takami, M., Rocha, J. M., Cajueiro, D. O., and Souza, S. R. (2014). Directed clustering coefficient as a measure of systemic risk in complex banking networks. Physica A: Statistical Mechanics and its Applications, 394(0):211–216.


• Tabak, B.M., Cajueiro, D.O., Serra, T.R., (2009), Topological properties of bank networks: the case of brazil, International Journal of Modern Physics C 20 (08).1121–1143.


• Upper, C. (2011). Simulation methods to assess the danger of contagion in interbank markets. Journal of Financial Stability, 7(3):111–125.


• Wells, S. (2002). UK interbank exposures: systemic risk implications. Financial Stability Review, 13(12), 175-182.


• Xu,T., He,J., Li, S., (2016).A dynamic network model for interbank market, Physica A.

Zhou, D., Stanley, H. E., D’Agostino, G., & Scala, A. (2012). Assortativity decreases the robustness of interdependent networks. Physical Review E, 86(6), 066103