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Cascades in Real Interbank Markets

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.

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Notes

  1. We choose this specification to ensure that bank’s capital is always consistent with interbank market exposures and does not fluctuate wildly. We obtained qualitatively similar simulation results by estimating \(\textit{TA}\) only from the current day’s activity, and for estimating \(\textit{TA}\) only from interbank lending. However, the results were more noisy, and, in the latter case, lead to an indeterminacy for banks that are only borrowing.

  2. The difference in the cascade size between 2006 and 2011 is not caused by a sudden change. We simulated the cascade size for all remaining years, and found that the average cascade size gradually increases, while the average degree of the network steadily decreases throughout the years.

  3. The results are very similar for the empirical network from 2006.

References

  • 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.

  • Arinaminpathy, N., Kapadia, S., & May, R. M. (2012). Size and complexity in model financial systems. PNAS, 109(45), 18338–18343.

    Article  Google Scholar 

  • Ashcraft, A., & Duffie, D. (2007). Systemic dynamics in the Federal Funds market. American Economic Review Papers and Proceedings, 97, 221–225.

    Article  Google Scholar 

  • Battiston, et al. (2012a). DebtRank: Too central to fail? Financial networks, the FED and systemic risk. Scientific Reports, 2, 541.

    Article  Google Scholar 

  • Battiston, et al. (2012b). Default cascades: When does risk diversification increase stability? Journal of Financial Stability, 8, 138–149.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Boss, M., Elsinger, H., Summer, H., & Thurner, M. (2006). Network topology of the interbank market. Quantitative Finance, 4(6), 677–684.

    Article  Google Scholar 

  • Cocco, J., Gomes, F., & Matins, N. (2009). Lending relationships in the interbank market. Journal of Financial Intermediation, 18, 24–48.

    Article  Google Scholar 

  • Craig, B., & von Peter, G. (2010). Interbank tiering and money center banks. Deutsche Bundesbank Discussion Paper, Series 2, 12/2010.

  • European Banking Authority, (2011). EU-Wide Stress Test Results. Available at www.eba.europa.eu.

  • Eisenberg, L., & Noe, T. H. (2001). Systemic risk in financial systems. Management Science, 47, 236–249.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Freeman, L. C. (1979). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239.

    Article  Google Scholar 

  • 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.

  • 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.

  • Furfine, C. (2003). Interbank exposures: Quantifying the risk of contagion. Journal of Money, Credit and Banking, 35(1), 111–128.

    Article  Google Scholar 

  • Gai, P., & Kapadia, S. (2010). Contagion in financial networks. Proceeding of the Royal Society A, 466, 2401–2423.

    Article  Google Scholar 

  • Glasserman, P., & Young, H. P. (2014). How likely is contagion in financial networks? Journal of Banking & Finance, in press.

  • 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.

    Article  Google Scholar 

  • Haldane, A. G., & May, R. M. (2011). Systemic risk in the banking ecosystems. Nature, 469, 351.

    Article  Google Scholar 

  • Hartmann, P., Manna, M., & Manzanares, A. (2001). The microstructure of the Euro money market. Journal of International Money and Finance, 20(6), 895–948.

    Article  Google Scholar 

  • Holme, P., & Saramäki, J. (2013). Temporal networks. Physics Report, 519, 97–125.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Karimi, F., & Holme, P. (2013). Threshold model of cascades in empirical temporal networks. Physica A, 392, 3476–3483.

  • 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.

    Article  Google Scholar 

  • Kitsak, et al. (2010). Identification of influencial spreaders in complex networks. Nature Physics, 6, 888–893.

    Article  Google Scholar 

  • Kok, C., & Montagna, M. (2013). Multi-layered interbank model for assessing systemic Risk, Kiel Working Paper No 1873.

  • Lux, T. (2011). Comment on financial systems: Ecology and economics. Nature, 469, 303.

    Article  Google Scholar 

  • Maslov, S., & Sneppen, K. (2002). Specificity and stability in topology of protein networks. Science, 296(5569), 910–913.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Mueller, J. (2006). Interbank credit lines as a channel of contagion. Journal of Financial Services Research, 29(1), 37–60.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Raddant, M. (2014). Structure in the Italian interbank loan market. Journal of International Money and Finance, 41, 197–213.

    Article  Google Scholar 

  • Roukny, T., et al. (2013). Default cascades in complex networks: Topology and systemic risk. Scientific Reports, 3, 2759.

    Article  Google Scholar 

  • Santos, E.B., & Cont, R. (2010). The Brazilian interbank network structure and systemic risk, Working paper Banco Central de Brazil 219.

  • Shin, H. S. (2008). Risk and liquidity in a system context. Journal of Financial Intermediation, 17(3), 315–329.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Upper, C., & Worms, A. (2002). Estimating bilateral exposures in the German interbank market: Is there a danger of contagion? Discussion Paper 09, Deutsche Bundesbank.

  • Vázquez, D. P., & Aizen, M. A. (2003). Null model analyses of specialization in plant-pollinator interactions. Ecology, 84(9), 2493–2501.

    Article  Google Scholar 

  • Watts, D. (2002). A simple model of global cascades on random networks. PNAS, 99, 5766–5771.

    Article  Google Scholar 

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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.

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Correspondence to Matthias Raddant.

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Karimi, F., Raddant, M. Cascades in Real Interbank Markets. Comput Econ 47, 49–66 (2016). https://doi.org/10.1007/s10614-014-9478-z

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Keywords

  • Interbank loan network
  • Systemic risk
  • Cascades
  • Null models