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Identifying Contagion in a Banking Network

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  • Alan Morrison
  • Michalis Vasios
  • Mungo Wilson
  • Filip Zikes

Abstract

We present the first micro-level evidence of the transmission of shocks through financial networks. Using the network of credit default swap (CDS) transactions between banks, we identify bank CDS returns attributable to counterparty losses. A bank's own CDS spread increases whenever counterparties from whom it has purchased default protection themselves experience losses. We find no such effect from losses of non-counterparties, nor from counterparties to whom the bank has sold protection. The effect on bank CDS returns through this counterparty loss channel is large relative to the direct effect on a bank's CDS returns from its own trading losses.

Suggested Citation

  • Alan Morrison & Michalis Vasios & Mungo Wilson & Filip Zikes, 2017. "Identifying Contagion in a Banking Network," Finance and Economics Discussion Series 2017-082, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgfe:2017-82
    DOI: 10.17016/FEDS.2017.082
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    Cited by:

    1. Hale, Galina & Lopez, Jose A., 2019. "Monitoring banking system connectedness with big data," Journal of Econometrics, Elsevier, vol. 212(1), pages 203-220.
    2. Shakya, Shasta, 2022. "Geographic networks and spillovers between banks," Journal of Corporate Finance, Elsevier, vol. 77(C).
    3. Riccardo Doyle, 2020. "Using Network Interbank Contagion in Bank Default Prediction," Papers 2005.12619, arXiv.org, revised May 2020.
    4. Park, Cyn-Young & Shin, Kwanho, 2018. "Global Banking Network and Regional Financial Contagion," ADB Economics Working Paper Series 546, Asian Development Bank.
    5. Park, Cyn-Young & Shin, Kwanho, 2020. "Contagion through National and Regional Exposures to Foreign Banks during the Global Financial Crisis," Journal of Financial Stability, Elsevier, vol. 46(C).
    6. Iman van Lelyveld, 2017. "The use of derivatives trade repository data: possibilities and challenges," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data needs and Statistics compilation for macroprudential analysis, volume 46, Bank for International Settlements.
    7. Petr Teply & Tomas Klinger, 2019. "Agent-based modeling of systemic risk in the European banking sector," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(4), pages 811-833, December.
    8. Joseph, Andreas & Vasios, Michalis, 2022. "OTC Microstructure in a period of stress: A Multi-layered network approach," Journal of Banking & Finance, Elsevier, vol. 138(C).
    9. Hossein Dastkhan, 2021. "Network‐based early warning system to predict financial crisis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 594-616, January.
    10. Ebrahimi Kahou, Mahdi & Lehar, Alfred, 2017. "Macroprudential policy: A review," Journal of Financial Stability, Elsevier, vol. 29(C), pages 92-105.
    11. Joseph, Andreas & Vasios, Michalis & Maizels, Olga & Shreyas, Ujwal & Tanner, John, 2019. "OTC microstructure in a period of stress: a multi‑layered network approach," Bank of England working papers 832, Bank of England.

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    More about this item

    Keywords

    Contagion; Counterparty risk; Credit default swaps; Networks;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation

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