Minimization of Systemic Risk for Directed Network Using Genetic Algorithm

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10199)

Abstract

In directed networks, flow dynamics may lead to cascade failures due to node and link removal. The systemic risk in financial systems follows similar mechanism, where banks are connected by interbank linkages with money transfers. A mathematical model of the banking network is used to investigate the relationships between the cascade dynamics and key parameters determining the banking network structure, including the connectivity, the bank’s capitalization, and the size of interbank exposure, based on analytical calculations and numerical simulations. To optimize the network topology for the minimization of systemic risk, genetic algorithm is applied to evolve the network. It is observed that the systemic risk of financial system could be decreased by increasing the degree variance of the associated network. This could be useful for financial risk management, with possible applications to other physical systems such as ecological web, where the network stability is also an important issue.

Keywords

Banking network Systemic risk Genetic algorithm Network optimization 

References

  1. 1.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52, 1059–1069 (2010)CrossRefGoogle Scholar
  2. 2.
    Burt, R., Kilduff, M., Tasselli, S.: Social network analysis: foundations and frontiers on advantage. Annu. Rev. Psychol. 64, 527–547 (2013)CrossRefGoogle Scholar
  3. 3.
    Barberán, A., Bates, S., Casamayor, E., Fierer, N.: Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 8, 952 (2014)CrossRefGoogle Scholar
  4. 4.
    Economides, N., Tåg, J.: Network neutrality on the internet: a two-sided market analysis. SSRN Electron. J. 24, 91–104 (2012)Google Scholar
  5. 5.
    Çetinkaya, E., Alenazi, M., Peck, A., Rohrer, J., Sterbenz, J.: Multilevel resilience analysis of transportation and communication networks. Telecommun. Syst. 60, 515–537 (2015)CrossRefGoogle Scholar
  6. 6.
    Schweitzer, F., Fagiolo, G., Sornette, D., Vega-Redondo, F., Vespignani, A., White, D.: Economic networks: the new challenges. Science 325, 422–425 (2009)MathSciNetMATHGoogle Scholar
  7. 7.
    Eisenberg, L., Noe, T.: Systemic risk in financial systems. Manag. Sci. 47, 236–249 (2001)CrossRefMATHGoogle Scholar
  8. 8.
    Feinstein, Z.: It’s a trap: emperor palpatine’s poison pill. ArXiv preprint arXiv:1511.09054 (2015)
  9. 9.
    Gleeson, J., Hurd, T., Melnik, S., Hackett, A.: Systemic Risk in Banking Networks Without Monte Carlo Simulation, vol. 2. Springer, Heidelberg (2012)Google Scholar
  10. 10.
    Haldane, A., May, R.: Systemic risk in banking ecosystems. Nature 469, 351–355 (2011)CrossRefGoogle Scholar
  11. 11.
    Albert, R., Jeong, H., Barabási, A.: Error and attack tolerance of complex networks. Nature 406, 378–382 (2000)CrossRefGoogle Scholar
  12. 12.
    Cohen, R., Erez, K., Ben-Avraham, D., Havlin, S.: Resilience of the internet to random breakdowns. Phys. Rev. Lett. 85, 4626–4628 (2000)CrossRefGoogle Scholar
  13. 13.
    Sornette, D., Deschâtres, F., Gilbert, T., Ageon, Y.: Endogenous versus exogenous shocks in complex networks: an empirical test using book sale rankings. Phys. Rev. Lett. 93, 228701 (2004)CrossRefGoogle Scholar
  14. 14.
    Gai, P., Kapadia, S.: Contagion in financial networks. Memeo, Bank of England (2007)Google Scholar
  15. 15.
    Iori, G., Jafarey, S., Padilla, F.: Systemic risk on the interbank market. J. Econ. Behav. Organ. 61, 525–542 (2006)CrossRefGoogle Scholar
  16. 16.
    Nier, E., Yang, J., Yorulmazer, T., Alentorn, A.: Network models and financial stability. J. Econ. Dyn. Control 31, 2033–2060 (2007)CrossRefMATHGoogle Scholar
  17. 17.
    Erdo, P., Rényi, A.: On random graphs. Publ. Math. 6, 290–297 (1959)MathSciNetGoogle Scholar
  18. 18.
    May, R., Arinaminpathy, N.: Systemic risk: the dynamics of model banking systems. J. R. Soc. Interface 7, 823–838 (2009)CrossRefGoogle Scholar
  19. 19.
    Chatterjee, S., Laudato, M.: Genetic algorithms in statistics: procedures and applications. Commun. Stat. Simul. 26, 1617–1630 (1997)MATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of PhysicsThe Hong Kong University of Science and TechnologyKowloonHong Kong

Personalised recommendations