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Contagion Risk in the Interbank Market: A Probabilistic Approach to Cope with Incomplete Structural Information

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  • Montagna, Mattia
  • Lux, Thomas

Abstract

Banks have become increasingly interconnected via interbank credit and other forms of liabilities. As a consequence of the increased interconnectedness, the failure of one node in the interbank network might constitute a threat to the survival of large parts of the entire system. How important this effect of "too-big-too-fail" and "too-interconnected-too-fail" is, depends on the exact topology of the network on which the supervisory authorities have typically very incomplete knowledge. We propose a probabilistic model to combine some important known quantities (like the size of the banks) with a realistic stochastic representation of the remaining structural elements. Our approach allows us to evaluate relevant measures for the contagion after default of one unit (i.e. number of expected subsequent defaults, or their probabilities). For some quantities we are able to derive closed form solutions, others can be obtained via computational mean-field approximations.

Suggested Citation

  • Montagna, Mattia & Lux, Thomas, 2014. "Contagion Risk in the Interbank Market: A Probabilistic Approach to Cope with Incomplete Structural Information," FinMaP-Working Papers 8, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
  • Handle: RePEc:zbw:fmpwps:8
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    References listed on IDEAS

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    Cited by:

    1. Giulio Cimini & Matteo Serri, 2016. "Entangling Credit and Funding Shocks in Interbank Markets," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-15, August.
    2. Marco Bardoscia & Stefano Battiston & Fabio Caccioli & Guido Caldarelli, 2015. "DebtRank: A Microscopic Foundation for Shock Propagation," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-13, June.
    3. Giulia Poce & Giulio Cimini & Andrea Gabrielli & Andrea Zaccaria & Giuditta Baldacci & Marco Polito & Mariangela Rizzo & Silvia Sabatini, 2016. "What do central counterparties default funds really cover? A network-based stress test answer," Papers 1611.03782, arXiv.org.
    4. Battiston Stefano & Caldarelli Guido & D’Errico Marco & Gurciullo Stefano, 2016. "Leveraging the network: A stress-test framework based on DebtRank," Statistics & Risk Modeling, De Gruyter, vol. 33(3-4), pages 117-138, December.
    5. González-Avella, Juan Carlos & de Quadros, Vanessa Hoffmann & Iglesias, José Roberto, 2016. "Network topology and interbank credit risk," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 235-243.
    6. Pawe{l} Smaga & Mateusz Wili'nski & Piotr Ochnicki & Piotr Arendarski & Tomasz Gubiec, 2016. "Can banks default overnight? Modeling endogenous contagion on O/N interbank market," Papers 1603.05142, arXiv.org.
    7. Lux, Thomas, 2016. "Network effects and systemic risk in the banking sector," FinMaP-Working Papers 62, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.

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    More about this item

    Keywords

    contagion; interbank market; network models;
    All these keywords.

    JEL classification:

    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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