Network versus portfolio structure in financial systems

Open Access
Regular Article


The question of how to stabilize financial systems has attracted considerable attention since the global financial crisis of 2007–2009. Recently, Beale et al. [Proc. Natl. Acad. Sci. USA 108, 12647 (2011)] demonstrated that higher portfolio diversity among banks would reduce systemic risk by decreasing the risk of simultaneous defaults at the expense of a higher likelihood of individual defaults. In practice, however, a bank default has an externality in that it undermines other banks’ balance sheets. This paper explores how each of these different sources of risk, simultaneity risk and externality, contributes to systemic risk. The results show that the allocation of external assets that minimizes systemic risk varies with the topology of the financial network as long as asset returns have negative correlations. In the model, a well-known centrality measure, PageRank, reflects an appropriately defined “infectiveness” of a bank. An important result is that the most infective bank needs not always to be the safest bank. Under certain circumstances, the most infective node should act as a firewall to prevent large-scale collective defaults. The introduction of a counteractive portfolio structure will significantly reduce systemic risk.


Statistical and Nonlinear Physics 


  1. 1.
    N. Beale, D.G. Rand, H. Battey, K. Croxson, R.M. May, M.A. Nowak, Proc. Natl. Acad. Sci. USA 108, 12647 (2011)ADSCrossRefGoogle Scholar
  2. 2.
    C. Upper, J. Financ. Stability 7, 111 (2011)CrossRefGoogle Scholar
  3. 3.
    R.M. May, N. Arinaminpathy, J. R. Soc. Interface 7, 823 (2010)CrossRefGoogle Scholar
  4. 4.
    S. Lenzu, G. Tedeschi, Physica. A 391, 4331 (2012)ADSCrossRefGoogle Scholar
  5. 5.
    P. Gai, S. Kapadia, P. Roy. Soc. A 466, 2401 (2010)MathSciNetADSCrossRefMATHGoogle Scholar
  6. 6.
    P. Gai, A. Haldane, S. Kapadia, J. Monetary Econ. 58, 453 (2011)CrossRefGoogle Scholar
  7. 7.
    E. Nier, J. Yang, T. Yorulmazer, A. Alentorn, J. Econ. Dyn. Control. 31, 2033 (2007)CrossRefMATHGoogle Scholar
  8. 8.
    F. Kyriakopoulos, S. Thurner, C. Puhr, S.W. Schmitz, Eur. Phys. J. B 71, 523 (2009)ADSCrossRefMATHGoogle Scholar
  9. 9.
    R. Ibragimov, D. Jaffee, J. Walden, J. Financ. Econ. 99, 333 (2011)CrossRefGoogle Scholar
  10. 10.
    W. Wagner, J. Financ. Intermed. 19, 373 (2010)CrossRefGoogle Scholar
  11. 11.
    W. Wagner, J. Finance 66, 1141 (2011)CrossRefGoogle Scholar
  12. 12.
    N. Rashevsky, Bull. Math. Biophys. 17, 229 (1955)MathSciNetCrossRefGoogle Scholar
  13. 13.
    E. Trucco, Bull. Math. Biophys. 18, 129 (1956)MathSciNetCrossRefGoogle Scholar
  14. 14.
    J. Kwapień, S. Drożdż, Phys. Rep. 515, 115 (2012)MathSciNetADSCrossRefGoogle Scholar
  15. 15.
    L. Eisenberg, T.H. Noe, Manage. Sci. 47, 236 (2001)CrossRefMATHGoogle Scholar
  16. 16.
    S. Brin, L. Page, Comput. Networks 30, 107 (1998)CrossRefGoogle Scholar
  17. 17.
    T. Wilhelm, J. Hollunder, Physica A 385, 385 (2007)MathSciNetADSCrossRefGoogle Scholar
  18. 18.
    A.-H. Sato, in Agent-Based Approaches in Economic and Social Complex Systems VI: Post-Proceedings of The AESCS International Workshop 2009 (Agent-Based Social Systems), edited by S.-H. Chen et al. (Springer, Tokyo, 2010), p. 3Google Scholar
  19. 19.
    T.R. Hurd, J.P. Gleeson, arXiv:1110.4312 (2011)Google Scholar
  20. 20.
    F. Caccioli, M. Shrestha, C. Moore, J.D. Farmer, arXiv:1210.5987 (2012)Google Scholar
  21. 21.
    K. Söramaki, M. Bech, J. Arnold, R. Glass, W. Beyeler, Physica A 379, 317 (2007)ADSCrossRefGoogle Scholar
  22. 22.
    G. Iori, G. De Masi, O.V. Precup, G. Gabbi, G. Caldarelli, J. Econ. Dyn. Control 32, 259 (2008)CrossRefMATHGoogle Scholar
  23. 23.
    K. Imakubo, Y. Soejima, Monetary and Economic Studies 28, 107 (2010)Google Scholar

Copyright information

© EDP Sciences, Società Italiana di Fisica and Springer-Verlag 2013

Authors and Affiliations

  1. 1.Graduate School of Economics, Kobe UniversityKobeJapan

Personalised recommendations