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A network model of the interbank market

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  • Li, Shouwei
  • He, Jianmin
  • Zhuang, Yaming

Abstract

This work introduces a network model of an interbank market based on interbank credit lending relationships. It generates some network features identified through empirical analysis. The critical issue to construct an interbank network is to decide the edges among banks, which is realized in this paper based on the interbank’s degree of trust. Through simulation analysis of the interbank network model, some typical structural features are identified in our interbank network, which are also proved to exist in real interbank networks. They are namely, a low clustering coefficient and a relatively short average path length, community structures, and a two-power-law distribution of out-degree and in-degree.

Suggested Citation

  • Li, Shouwei & He, Jianmin & Zhuang, Yaming, 2010. "A network model of the interbank market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5587-5593.
    • Handle: RePEc:eee:phsmap:v:389:y:2010:i:24:p:5587-5593
    DOI: 10.1016/j.physa.2010.08.057
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    Citations

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    Cited by:

    1. Kuzubaş, Tolga Umut & Ömercikoğlu, Inci & Saltoğlu, Burak, 2014. "Network centrality measures and systemic risk: An application to the Turkish financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 203-215.
    2. Solange Maria Guerra & Benjamin Miranda Tabak & Rodrigo Cesar de Castro Miranda, 2014. "Do Interconnections Matter for Bank Efficiency?," Working Papers Series 374, Central Bank of Brazil, Research Department.
    3. Papadimitriou, Theophilos & Gogas, Periklis & Tabak, Benjamin M., 2013. "Complex networks and banking systems supervision," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4429-4434.
    4. Jenna Birch & Athanasios A. Pantelous & Kimmo Soramäki, 2016. "Analysis of Correlation Based Networks Representing DAX 30 Stock Price Returns," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 501-525, April.
    5. Teteryatnikova, Mariya, 2014. "Systemic risk in banking networks: Advantages of “tiered” banking systems," Journal of Economic Dynamics and Control, Elsevier, vol. 47(C), pages 186-210.
    6. Sokolov, Andrey & Webster, Rachel & Melatos, Andrew & Kieu, Tien, 2012. "Loan and nonloan flows in the Australian interbank network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(9), pages 2867-2882.
    7. He, Fang & Chen, Xi, 2016. "Credit networks and systemic risk of Chinese local financing platforms: Too central or too big to fail?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 158-170.
    8. V. A. Kalyagin & A. P. Koldanov & P. A. Koldanov & P. M. Pardalos & V. A. Zamaraev, 2013. "Measures of uncertainty in market network analysis," Papers 1311.2273, arXiv.org.
    9. Ragoobar, Tricia & Whalley, Jason & Harle, David, 2011. "Public and private intervention for next-generation access deployment: Possibilities for three European countries," Telecommunications Policy, Elsevier, vol. 35(9), pages 827-841.
    10. Nguyen, Q. & Nguyen, N.K. K. & Nguyen, L.H. N., 2019. "Dynamic topology and allometric scaling behavior on the Vietnamese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 235-243.
    11. He, Jianmin & Sui, Xin & Li, Shouwei, 2016. "An endogenous model of the credit network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 1-14.
    12. Xu, Tao & He, Jianmin & Li, Shouwei, 2016. "A dynamic network model for interbank market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 131-138.
    13. Kalyagin, V.A. & Koldanov, A.P. & Koldanov, P.A. & Pardalos, P.M. & Zamaraev, V.A., 2014. "Measures of uncertainty in market network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 59-70.

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