Computational Economics

, Volume 47, Issue 1, pp 49–66 | Cite as

Cascades in Real Interbank Markets

Article

Abstract

We analyze cascades of defaults in an interbank loan market. The novel feature of this study is that the network structure and the size distribution of banks are derived from empirical data. We find that the ability of a defaulted institution to start a cascade depends on an interplay of shock size and connectivity. Further results indicate that the interbank loan network is structurally less stable after the financial crisis than it was before. To evaluate the influence of the network structure on market stability, we compare simulated cascades from the empirical network with results from different network models. The results show that the empirical network has non-random features, which cannot be captured by randomized networks. The analysis also reveals that simulations that assume homogeneity for banks and loan size tend to overestimate the fragility of the interbank market.

Keywords

Interbank loan network Systemic risk Cascades  Null models 

Notes

Acknowledgments

The authors thank Petter Holme, Martin Rosvall, and Thomas Lux for helpful discussions. FK thanks the Swedish Research Council. MR thanks the Leibniz Association for partial funding of this project.

References

  1. Arciero, L. et al. (2013). How to measure the unsecured money market? The Eurosystem’s implementation and validation using TARGET2 data, DNB Working Paper No. 369.Google Scholar
  2. Arinaminpathy, N., Kapadia, S., & May, R. M. (2012). Size and complexity in model financial systems. PNAS, 109(45), 18338–18343.CrossRefGoogle Scholar
  3. Ashcraft, A., & Duffie, D. (2007). Systemic dynamics in the Federal Funds market. American Economic Review Papers and Proceedings, 97, 221–225.CrossRefGoogle Scholar
  4. Battiston, et al. (2012a). DebtRank: Too central to fail? Financial networks, the FED and systemic risk. Scientific Reports, 2, 541.CrossRefGoogle Scholar
  5. Battiston, et al. (2012b). Default cascades: When does risk diversification increase stability? Journal of Financial Stability, 8, 138–149.CrossRefGoogle Scholar
  6. Beaupain, R., & Durré, A. (2011). Inferring trading dynamics for an OTC market: The case of the euro area overnight money market. Quantitative Finance, 11(9), 1285–1295.CrossRefGoogle Scholar
  7. Boss, M., Elsinger, H., Summer, H., & Thurner, M. (2006). Network topology of the interbank market. Quantitative Finance, 4(6), 677–684.CrossRefGoogle Scholar
  8. Cocco, J., Gomes, F., & Matins, N. (2009). Lending relationships in the interbank market. Journal of Financial Intermediation, 18, 24–48.CrossRefGoogle Scholar
  9. Craig, B., & von Peter, G. (2010). Interbank tiering and money center banks. Deutsche Bundesbank Discussion Paper, Series 2, 12/2010.Google Scholar
  10. European Banking Authority, (2011). EU-Wide Stress Test Results. Available at www.eba.europa.eu.
  11. Eisenberg, L., & Noe, T. H. (2001). Systemic risk in financial systems. Management Science, 47, 236–249.CrossRefGoogle Scholar
  12. Fagiolo, G., Squartini, T., & Garlaschelli, D. (2013). Null models of economic networks: The case of the world trade web. Journal of Economic Interaction and Coordination, 8(1), 1–33.CrossRefGoogle Scholar
  13. Freeman, L. C. (1979). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239.CrossRefGoogle Scholar
  14. Fricke, D., & Lux, T. (2013). On the distribution of links in the interbank network: Evidence from the e-Mid overnight money market, Kiel Working Papers, No. 1819.Google Scholar
  15. Fricke, D., & Lux, T. (2014). Core-periphery structure in the overnight money market: Evidence from the e-mid trading platform. Computational Economics. doi: 10.1007/s10614-014-9427-x.
  16. Furfine, C. (2003). Interbank exposures: Quantifying the risk of contagion. Journal of Money, Credit and Banking, 35(1), 111–128.CrossRefGoogle Scholar
  17. Gai, P., & Kapadia, S. (2010). Contagion in financial networks. Proceeding of the Royal Society A, 466, 2401–2423.CrossRefGoogle Scholar
  18. Glasserman, P., & Young, H. P. (2014). How likely is contagion in financial networks? Journal of Banking & Finance, in press.Google Scholar
  19. Goltsev, A. V., Dorogovtsev, S. N., & Mendes, J. F. F. (2006). k-core (bootstrap) percolation on complex networks: Critical phenomena and nonlocal effects. PRE, 73(5), 056101.CrossRefGoogle Scholar
  20. Haldane, A. G., & May, R. M. (2011). Systemic risk in the banking ecosystems. Nature, 469, 351.CrossRefGoogle Scholar
  21. Hartmann, P., Manna, M., & Manzanares, A. (2001). The microstructure of the Euro money market. Journal of International Money and Finance, 20(6), 895–948.CrossRefGoogle Scholar
  22. Holme, P., & Saramäki, J. (2013). Temporal networks. Physics Report, 519, 97–125.CrossRefGoogle Scholar
  23. Iori, G., de Masi, G., Precup, O., Gabbi, G., & Caldarelli, G. (2008). A network analysis of the Italian overnight money market. Journal of Economic Dynamics & Control, 32, 259–278.CrossRefGoogle Scholar
  24. Isella, L., Stehlé, J., Barrat, A., Cattuto, C., Pinton, J. F., & Van den Broeck, W. (2011). What’s in a crowd? Analysis of face-to-face behavioral networks. Journal of Theoretical Biology, 271(1), 166–180.CrossRefGoogle Scholar
  25. Karimi, F., & Holme, P. (2013). Threshold model of cascades in empirical temporal networks. Physica A, 392, 3476–3483.Google Scholar
  26. Karsai, M., Kivelä, M., Pan, R. K., Kaski, K., Kertész, J., Barabási, A. L., et al. (2011). Small but slow world: How network topology and burstiness slow down spreading. Physical Review E, 83(2), 025102.CrossRefGoogle Scholar
  27. Kitsak, et al. (2010). Identification of influencial spreaders in complex networks. Nature Physics, 6, 888–893.CrossRefGoogle Scholar
  28. Kok, C., & Montagna, M. (2013). Multi-layered interbank model for assessing systemic Risk, Kiel Working Paper No 1873.Google Scholar
  29. Lux, T. (2011). Comment on financial systems: Ecology and economics. Nature, 469, 303.CrossRefGoogle Scholar
  30. Maslov, S., & Sneppen, K. (2002). Specificity and stability in topology of protein networks. Science, 296(5569), 910–913.CrossRefGoogle Scholar
  31. Mistrulli, P. E. (2011). Assessing financial contagion in the interbank market: Maximum entropy versus observed interbank lending patterns. Journal of Banking & Finance, 35(5), 1114–1127.CrossRefGoogle Scholar
  32. Mueller, J. (2006). Interbank credit lines as a channel of contagion. Journal of Financial Services Research, 29(1), 37–60.CrossRefGoogle Scholar
  33. Nier, E., Yang, J., Yorulmazer, T., & Alentorn, A. (2007). Network models and financial stability. Journal of Economic Dynamics & Control, 31, 2033–2060.CrossRefGoogle Scholar
  34. Raddant, M. (2014). Structure in the Italian interbank loan market. Journal of International Money and Finance, 41, 197–213.CrossRefGoogle Scholar
  35. Roukny, T., et al. (2013). Default cascades in complex networks: Topology and systemic risk. Scientific Reports, 3, 2759.CrossRefGoogle Scholar
  36. Santos, E.B., & Cont, R. (2010). The Brazilian interbank network structure and systemic risk, Working paper Banco Central de Brazil 219.Google Scholar
  37. Shin, H. S. (2008). Risk and liquidity in a system context. Journal of Financial Intermediation, 17(3), 315–329.CrossRefGoogle Scholar
  38. Soramäki, K., Bech, M., Arnold, J., Glass, R. J., & Beyeler, W. E. (2007). The topology of interbank payment flows. Physica A, 379(1), 317–333.CrossRefGoogle Scholar
  39. Upper, C., & Worms, A. (2002). Estimating bilateral exposures in the German interbank market: Is there a danger of contagion? Discussion Paper 09, Deutsche Bundesbank.Google Scholar
  40. Vázquez, D. P., & Aizen, M. A. (2003). Null model analyses of specialization in plant-pollinator interactions. Ecology, 84(9), 2493–2501.CrossRefGoogle Scholar
  41. Watts, D. (2002). A simple model of global cascades on random networks. PNAS, 99, 5766–5771.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Physics, IcelabUmeå UniversityUmeåSweden
  2. 2.Kiel Institute (IfW), and CAU KielDepartment of EconomicsKielGermany

Personalised recommendations