An Interbank Market Network Model based on Bank Credit Lending Preference

Tao Xu, Jianmin He, Shouwei Li

Abstract

An interbank market network model based on bank credit lending preference is built in this paper to explain the formation mechanism of interbank market network structure. As well, we analyze the impact of credit lending risk preference on network topology structure, which includes degree distribution, network clustering coefficient, average shortest path length and network efficiency. Simulation results demonstrate that the accumulation degree follows dual power law distribution with credit lending risk preference parameter value equal or greater than 1, while the accumulation degree follows power law distribution with credit lending risk preference parameter value smaller than 1. The interbank market network shows small world topology property. With the increasing of bank credit lending risk preference, the average shortest path length decreases but network efficiency improves, which enhances the stability of the network.

References

  1. Allen, F., & Gale, D., 2000. Financial contagion. Journal of political economy, 108(1), 1-33.
  2. Barabási, A. L., & Albert, R., 1999. Emergence of scaling in random networks. Science, 286(5439), 509-512.
  3. Becher, C., Millard, S., & Soramaki, K., 2008. The network topology of CHAPS Sterling.
  4. Boginski, V., Butenko, S., & Pardalos, P. M., 2005. Statistical analysis of financial networks. Computational statistics & data analysis, 48(2), 431-443.
  5. Bonanno, G., Caldarelli, G., Lillo, F., Miccichè, S., Vandewalle, N., & Mantegna, R. N., 2004. Networks of equities in financial markets. The European Physical Journal B-Condensed Matter and Complex Systems, 38(2), 363-371.
  6. Boss, M., Elsinger, H., Summer, M., & Thurner 4, S., 2004. Network topology of the interbank market. Quantitative Finance, 4(6), 677-684.
  7. Burda, Z., Jurkiewicz, J., & Krzywicki, A., 2004. Statistical mechanics of random graphs. Physica A: Statistical Mechanics and its Applications, 344(1), 56- 61.
  8. 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.
  9. Freixas, X., Parigi, B. M., & Rochet, J. C., 2000. Systemic risk, interbank relations, and liquidity provision by the central bank. Journal of money, credit and banking, 611-638.
  10. Garlaschelli, D., & Loffredo, M. I., 2004. Fitnessdependent topological properties of the world trade web. Physical review letters, 93(18), 188701.
  11. Huang, W. Q., Zhuang, X. T., & Yao, S., 2009. A network analysis of the Chinese stock market. Physica A: Statistical Mechanics and its Applications, 388(14), 2956-2964.
  12. Inaoka, H., Takayasu, H., Shimizu, T., Ninomiya, T., & Taniguchi, K., 2004. Self-similarity of banking network. Physica A: Statistical Mechanics and its Applications, 339(3), 621-634.
  13. Iori, G., De Masi, G., Precup, O. V., Gabbi, G., & Caldarelli, G., 2008. A network analysis of the Italian overnight money market. Journal of Economic Dynamics and Control, 32(1), 259-278.
  14. 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.
  15. Li, S., He, J., & Zhuang, Y., 2010. A network model of the interbank market.Physica A: Statistical Mechanics and its Applications, 389(24), 5587-5593.
  16. Martínez-Jaramillo, S., Pérez, O. P., Embriz, F. A., & Dey, F. L. G., 2010. Systemic risk, financial contagion and financial fragility. Journal of Economic Dynamics and Control, 34(11), 2358-2374.
  17. Reichardt, J., & Bornholdt, S., 2005. Economic networks and social communities in online-auction sites (No. physics/0503138).
  18. Soramäki, 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.
  19. Souma, W., Fujiwara, Y., & Aoyama, H., 2003. Complex networks and economics. Physica A: Statistical Mechanics and its Applications, 324(1), 396-401.
  20. 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.
  21. Watts, D. J., & Strogatz, S. H., 1998. Collective dynamics of 'small-world'networks. Nature, 393(6684), 440- 442.
Download


Paper Citation


in Harvard Style

Xu T., He J. and Li S. (2016). An Interbank Market Network Model based on Bank Credit Lending Preference . In Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-171-7, pages 157-162. DOI: 10.5220/0005734201570162


in Bibtex Style

@conference{icores16,
author={Tao Xu and Jianmin He and Shouwei Li},
title={An Interbank Market Network Model based on Bank Credit Lending Preference},
booktitle={Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2016},
pages={157-162},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005734201570162},
isbn={978-989-758-171-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - An Interbank Market Network Model based on Bank Credit Lending Preference
SN - 978-989-758-171-7
AU - Xu T.
AU - He J.
AU - Li S.
PY - 2016
SP - 157
EP - 162
DO - 10.5220/0005734201570162