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Cascades in multiplex financial networks with debts of different seniority

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  • Charles D. Brummitt
  • Teruyoshi Kobayashi

Abstract

The seniority of debt, which determines the order in which a bankrupt institution repays its debts, is an important and sometimes contentious feature of financial crises, yet its impact on system-wide stability is not well understood. We capture seniority of debt in a multiplex network, a graph of nodes connected by multiple types of edges. Here, an edge between banks denotes a debt contract of a certain level of seniority. Next we study cascading default. There exist multiple kinds of bankruptcy, indexed by the highest level of seniority at which a bank cannot repay all its debts. Self-interested banks would prefer that all their loans be made at the most senior level. However, mixing debts of different seniority levels makes the system more stable, in that it shrinks the set of network densities for which bankruptcies spread widely. We compute the optimal ratio of senior to junior debts, which we call the optimal seniority ratio, for two uncorrelated Erdos-Renyi networks. If institutions erode their buffer against insolvency, then this optimal seniority ratio rises; in other words, if default thresholds fall, then more loans should be senior. We generalize the analytical results to arbitrarily many levels of seniority and to heavy-tailed degree distributions.

Suggested Citation

  • Charles D. Brummitt & Teruyoshi Kobayashi, 2015. "Cascades in multiplex financial networks with debts of different seniority," Papers 1501.05400, arXiv.org, revised May 2015.
  • Handle: RePEc:arx:papers:1501.05400
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    Cited by:

    1. Li, Shouwei & Liu, Yifu & Wu, Chaoqun, 2020. "Systemic risk in bank-firm multiplex networks," Finance Research Letters, Elsevier, vol. 33(C).
    2. Fabio Caccioli & Paolo Barucca & Teruyoshi Kobayashi, 2018. "Network models of financial systemic risk: a review," Journal of Computational Social Science, Springer, vol. 1(1), pages 81-114, January.
    3. Teruyoshi Kobayashi, 2015. "Trend-driven information cascades on random networks," Discussion Papers 1529, Graduate School of Economics, Kobe University.
    4. Teruyoshi Kobayashi & Anna Sapienza & Emilio Ferrara, 2018. "Extracting the multi-timescale activity patterns of online financial markets," Papers 1802.07405, arXiv.org, revised Apr 2018.
    5. Kobayashi, Teruyoshi & Takaguchi, Taro, 2018. "Identifying relationship lending in the interbank market: A network approach," Journal of Banking & Finance, Elsevier, vol. 97(C), pages 20-36.
    6. Teruyoshi Kobayashi & Taro Takaguchi, 2017. "Significant ties: Identifying relationship lending in temporal interbank networks," Discussion Papers 1717, Graduate School of Economics, Kobe University.
    7. Wang, Jianwei & Cai, Lin & Xu, Bo & Li, Peng & Sun, Enhui & Zhu, Zhiguo, 2016. "Out of control: Fluctuation of cascading dynamics in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 1231-1243.
    8. De Caux, Robert & McGroarty, Frank & Brede, Markus, 2017. "The evolution of risk and bailout strategy in banking systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 109-118.
    9. X. Zhang & L. D. Valdez & H. E. Stanley & L. A. Braunstein, 2019. "Modeling Risk Contagion in the Venture Capital Market: A Multilayer Network Approach," Complexity, Hindawi, vol. 2019, pages 1-11, December.

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