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Assessing the impact of incomplete information on the resilience of financial networks

  • S.I.: Recent Developments in Financial Modeling and Risk Management
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Abstract

This paper explores the issue of robustness against failure cascades for the network of interbank exposures. The available data were retrieved through the Bank for International Settlements database and report only incomplete information from which networks displaying a core-periphery structure were produced. A model of financial contagion was set up to estimate the width and length of the cascades, and was run on the networks detected from the data, as well as simulated data. The role of incomplete information was taken into account by considering a worst-case scenario in which unobserved links were assumed to be present. Given the core-periphery structure of the network, the worst-case scenario was studied in different sub-cases in which different periphery organisations were considered. Simulations showed that the actual network was far from the worst scenario for the propagation of contagion, meaning that the role of unobserved links can substantially alter the resilience of the whole network.

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Notes

  1. Reporting countries Australia, Austria, Belgium, Brazil, Canada, Chile, Chinese Taipei, Denmark, Finland, France, Germany, Greece, Hong Kong SAR, India, Ireland, Italy, Japan, Luxembourg, Mexico, Netherlands, Norway, Panama, Portugal, Singapore, South Korea, South Korea, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States. Counterpart countries Afghanistan, Albania, Algeria, Andorra, Angola, Argentina, Armenia, Aruba, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belize, Benin, Bermuda, Bhutan, Bolivia, Bonaire, Sint Eustatius and Saba, Bosnia and Herzegovina, Botswana, Brunei, Bulgaria, Burkina Faso, Burundi, Cambodia, Cameroon, Cape Verde, Cayman Islands, Central African Republic, Chad, China, Colombia, Comoros, Congo, Congo Democratic Republic, Costa Rica, Cote d’Ivoire, Croatia, Cuba, Curacao Cyprus, Czech Republic, Czechoslovakia, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Ethiopia, Faeroe Islands, Falkland Islands, Fiji, French Polynesia, Gabon, Gambia, Georgia, German Democratic Republic, Ghana, Gibraltar, Greenland, Grenada, Guatemala, Guernsey, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, Indonesia, Iran, Iraq, Isle of Man, Israel, Jamaica, Jersey, Jordan, Kazakhstan, Kenya, Kiribati, Kuwait, Kyrgyz Republic, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Liechtenstein, Lithuania, Macao SAR, Macedonia (FYR), Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Micronesia, Moldova, Mongolia, Montenegro Morocco, Mozambique, Myanmar, Nauru, Nepal, Netherlands Antilles, New Caledonia, New Zealand, Nicaragua, Nigeria, North Korea, Oman, Pakistan, Palau, Palestinian Territory, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Qatar, Romania, Russia Rwanda, Samoa, San Marino, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Serbia and Montenegro, Seychelles, Sierra Leone, Sint Maarten, Slovakia, Slovenia, Solomon Islands, Somalia South Africa, South Sudan, Soviet Union, Sri Lanka, St. Helena and Dependencies, St. Lucia, St. Vincent and the Grenadines Sudan, Suriname Swaziland, Syria, Tajikistan, Tanzania, Thailand, Timor Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkmenistan Turks and Caicos Islands, Tuvalu, Uganda, Ukraine, United Arab Emirates, Uruguay, US Pacific Islands, Uzbekistan, Vanuatu Vatican City State, Venezuela, Vietnam, Wallis and Futuna, Yemen, Yugoslavia, Zambia, Zimbabwe.

  2. G-SIB banks (Global Systemically Important Banks) are subject to more stringent requirements. See http://www.fsb.org/2017/11/fsb-publishes-2017-g-sib-list/ for the updated list of banks and requirements.

References

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

    Google Scholar 

  • Albert, R., Jeong, H., & Barabási, A.-L. (2000). Error and attack tolerance of complex networks. Nature, 406, 378–382.

    Google Scholar 

  • Allen, F., & Gale, D. (2000). Financial contagion. The Journal of Political Economy, 108(1), 1–33.

    Google Scholar 

  • Allen, F., & Babus, A. (2008). Networks in finance. Wharton Financial Institutions Center Working Paper No. 08–07.

  • Barrat, A., Barthelemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. PNAS, 101(11), 3747–3752.

    Google Scholar 

  • Bellenzier, L., Vitting Andersen, J., & Rotundo, G. (2016). Contagion in the worlds stocks exchanges seen as set of coupled oscillators. Economic Modelling, 59, 224–236.

    Google Scholar 

  • BIS database www.bis.org.

  • Bongini, P., Clemente, G. P., & Grassi, R. (2018). Interconnectedness, G-SIBs and network dynamics of global banking. Finance Research Letters, 27(C), 185–192.

    Google Scholar 

  • Borgatti, S. P., Carley, K. M., & Krackhardt, D. (2006). On the robustness of centrality measures under conditions of imperfect data. Social Networks, 28(2), 124–136.

    Google Scholar 

  • Bovi, M., & Cerqueti, R. (2016). Forecasting macroeconomic fundamentals in economic crises. Annals of Operations Research, 247, 451–469.

    Google Scholar 

  • Browne, K. (2005). Snowball sampling: Using social networks to research non-heterosexual women. International Journal of Social Research Methodology, 8(1), 47–60.

    Google Scholar 

  • Cerqueti, R., Rotundo, G., & Ausloos, M. (2018). Investigating the configurations in cross-shareholding: A joint copula. Entropy, 20, 134.

    Google Scholar 

  • Cinelli, M. (2019). Generalized rich-club ordering in networks. Journal of Complex Networks. https://doi.org/10.1093/comnet/cnz002.

  • Cinelli, M., Ferraro G. & Iovanella A. (2017). Resilience of core-periphery networks in the case of rich-club. Complexity, 2017 Article ID 6548362.

  • Cinelli, M., Ferraro, G., & Iovanella, A. (2018). Rich-club ordering and the dyadic effect: Two interrelated phenomena. Physica A, 490, 808–818.

    Google Scholar 

  • Clemente, G. P., & Grassi, R. (2018). Directed clustering in weighted networks: A new perspective. Chaos, Solitons & Fractals, 107, 26–38.

    Google Scholar 

  • Csermely, P., London, A., Wu, L. Y., & Uzzi, B. (2013). Structure and dynamics of core/periphery networks. Journal of Complex Networks, 1(2), 93–123.

    Google Scholar 

  • D’Agostino, G., Scala, A., Zlatic, V., & Caldarelli, G. (2012). Robustness and assortativity for diffusion-like processes in scale-free networks. EPL (Europhysics Letters), 97(6), 68006.

    Google Scholar 

  • Eisenberg, L., & Noe, T. H. (2001). Systemic risk in financial systems. Management Science, 47(2), 236–249.

    Google Scholar 

  • Elliott, M., Golub, B., & Jackson, M. O. (2014). Financial networks and contagion. American Economic Review, 104(10), 3115–53.

    Google Scholar 

  • European Central Bank. (2010). Analytical models and tools for the identification and assessment of systemic risks. Financial Stability Review, 1, 38–46.

  • Fortunato, S., & Hric, D. (2016). Community detection in networks: A user guide. Physics Reports, 659, 1–44.

    Google Scholar 

  • Fritz, O., Christen, E., Sinabell, F. & Hinz, J. (2017). Russias and the EUs sanctions: Economic and trade effects, compliance and the way forward. Report for the European Parliament’s Committee on International Trade October 2017-PE 603.847. ISBN: 978-92-846-1740-1. https://doi.org/10.2861/658339.

  • Gai, P., & Kapadia, S. (2010). Contagion in financial networks. Proceedings of the Royal Society A. https://doi.org/10.1098/rspa.2009.0410.

  • Garas, A., Argyrakis, P., Rozenblat, C., Tomassini, M., & Havlin, Shlomo. (2010). Worldwide spreading of economic crisis. New Journal of Physics, 12, 113043.

    Google Scholar 

  • Glasserman, P., & Young, H. P. (2016). Contagion in financial networks. Journal of Economic Literature, 54(3), 779–831.

  • Gutfraind, A. (2012). Optimizing network topology for cascade resilience, Springer optimization and Its applications. In M. Thai & P. Pardalos (Eds.), Handbook of optimization in complex networks. New York: Springer.

    Google Scholar 

  • Haldane, A. G., & May, R. M. (2011). Systemic risk in banking ecosystems. Nature, 469, 351–355.

    Google Scholar 

  • Hoffmann, T., Peel, L., Lambiotte, R. & Jones, N. S. (2018). Community detection in networks with unobserved edges. arXiv:1808.06079.

  • Hübsch, A., & Walther, U. (2017). The impact of network inhomogeneities on contagion and system stability. Annals of Operation Research, 254, 61–87.

    Google Scholar 

  • Karimi, F., & Raddant, M. (2016). Cascades in real interbank markets. Computational Economics, 47(1), 49–66.

    Google Scholar 

  • Kojaku, S., Cimini, G., Caldarelli, G., & Masuda, N. (2018). Structural changes in the interbank market across the financial crisis from multiple core-periphery analysis. Journal of Network Theory in Finance, 4(3), 33–51.

    Google Scholar 

  • Lambiotte, R., & Ausloos, M. (2007). Growing network with j-redirection. Europhysics Letters, 77, 58002.

    Google Scholar 

  • Lux, T. (2017). Network effects and systemic risk in the banking sector. In Monetary policy, financial crises, and the macroeconomy. https://doi.org/10.1007/978-3-319-56261-2_4

  • Massad, N., & Andersen, J. V. (2018). Three different ways synchronization can cause contagion in financial markets. Risks, 6, 104.

    Google Scholar 

  • Minoiu, C., & Reyes, J. A. (2013). A network analysis of global banking: 1978–2010. Journal of Financial Stability, 9, 168–184.

    Google Scholar 

  • Marchette, D. J., & Priebe, C. E. (2008). Predicting unobserved links in incompletely observed networks. Computational Statistics & Data Analysis, 52(3), 1373–1386.

    Google Scholar 

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

    Google Scholar 

  • Nagurney, A., & Ke, K. (2006). Financial networks with intermediation: Risk management with variable weights. European Journal of Operation Research, 172, 40–63.

    Google Scholar 

  • Nagurney, A., & Siokos, S. (1998). Network modeling of international financial equilibria with hedging. Annals of Operation Research, 82, 139–160.

    Google Scholar 

  • Newman, M. E. J. (2018). Network structure from rich but noisy data. Nature Physics, 14(6), 542.

    Google Scholar 

  • Newman, M. E. J. (2018). Network reconstruction and error estimation with noisy network data. arXiv:1803.02427.

  • Newman, M., Strogatz, S., & Watts, D. (2001). Random graphs with arbitrary degree distributions and their applications. Physical Review E, 64(026), 118.

    Google Scholar 

  • Peron, T. K., Costa, L. D. A. F., & Rodrigues, F. A. (2012). The structure and resilience of financial market networks. Chaos, 22, 013117.

    Google Scholar 

  • Rotundo, G., & D’Arcangelis, A. M. (2014). Network of companies: An analysis of market concentration in the Italian stock market. Quality & Quantity, 48(4), 1893–1910.

    Google Scholar 

  • Roukny, T., Bersini, H., Pirotte, H., Caldarelli, G., & Battiston, S. (2013). Default cascades in complex networks: Topology and systemic risk. Scientific Reports, 3, 2759.

    Google Scholar 

  • Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. Neuroimage, 52(3), 1059–1069.

    Google Scholar 

  • Schneider, C. M., Moreira, A. A., Andrade, J. S., Havlin, S., & Herrmann, H. J. (2011). Mitigation of malicious attacks on networks. Proceedings of the National Academy of Sciences, 108(10), 3838–3841.

    Google Scholar 

  • Serri, M., Caldarelli, G., & Cimini, G. (2017). How the interbank market becomes systemically dangerous: An agent-based network model of financial distress propagation. Journal of Network Theory in Finance, 3(1), 1–18.

    Google Scholar 

  • Silva, W., Kimura, H., & Sobreiro, V. A. (2017). An analysis of the literature on systemic financial risk: A survey. Journal of Financial Stability, 28, 91–114.

    Google Scholar 

  • Xu, X. K., Zhang, J., & Small, M. (2010). Rich-club connectivity dominates assortativity and transitivity of complex networks. Physical Review E, 82(4), 046117.

    Google Scholar 

  • Zhou, S., & Mondragón, R. J. (2004). The rich-club phenomenon in the Internet topology. IEEE Communications Letters, 8(3), 180–182.

    Google Scholar 

  • Zhu, Y., Yang, F., & Ye, W. (2018). Financial contagion behavior analysis based on complex network approach. Annals of Operation Research, 268, 93–111.

    Google Scholar 

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Authors and Affiliations

  1. Department of Enterprise Engineering, University of Rome Tor Vergata, Via del Politecnico, 1, 00133, Rome, Italy

    Matteo Cinelli, Giovanna Ferraro & Antonio Iovanella

  2. Department of Statistical Sciences, Sapienza University of Rome, Piazzale A. Moro 5, 00185, Rome, Italy

    Giulia Rotundo

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  1. Matteo Cinelli
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  2. Giovanna Ferraro
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  3. Antonio Iovanella
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  4. Giulia Rotundo
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Correspondence to Antonio Iovanella.

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Appendix A

Appendix A

See Fig. 10.

Fig. 10
figure 10

An example of cascade sequence for the BIS network \(2005\_1\) in the case of initial node with out-degree not zero. Nodes in the cascade are depicted in red. (Color figure online)

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Cinelli, M., Ferraro, G., Iovanella, A. et al. Assessing the impact of incomplete information on the resilience of financial networks. Ann Oper Res 299, 721–745 (2021). https://doi.org/10.1007/s10479-019-03306-y

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