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Too Connected to Fail? Inferring Network Ties From Price Co-Movements

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  • Jakob J. Bosma
  • Michael Koetter
  • Michael Wedow

Abstract

We use extreme value theory methods to infer conventionally unobservable connections between financial institutions from joint extreme movements in credit default swap spreads and equity returns. Estimated pairwise co-crash probabilities identify significant connections among up to 186 financial institutions prior to the crisis of 2007/2008. Financial institutions that were very central prior to the crisis were more likely to be bailed out during the crisis or receive the status of systemically important institutions. This result remains intact also after controlling for indicators of too-big-to-fail concerns, systemic, systematic, and idiosyncratic risks. Both credit default swap (CDS)-based and equity-based connections are significant predictors of bailouts. Supplementary materials for this article are available online.

Suggested Citation

  • Jakob J. Bosma & Michael Koetter & Michael Wedow, 2019. "Too Connected to Fail? Inferring Network Ties From Price Co-Movements," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(1), pages 67-80, January.
    • Handle: RePEc:taf:jnlbes:v:37:y:2019:i:1:p:67-80
    DOI: 10.1080/07350015.2016.1272459
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    Citations

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    Cited by:

    1. Chen, Yu & Gao, Yu & Shu, Lei & Zhu, Xiaonan, 2023. "Network effects on risk co-movements: A network quantile autoregression-based analysis," Finance Research Letters, Elsevier, vol. 56(C).
    2. Gian Paolo Clemente & Rosanna Grassi & Chiara Pederzoli, 2020. "Networks and market-based measures of systemic risk: the European banking system in the aftermath of the financial crisis," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(1), pages 159-181, January.
    3. Koetter, Michael & Müller, Carola & Noth, Felix & Fritz, Benedikt, 2018. "May the force be with you: Exit barriers, governance shocks, and profitability sclerosis in banking," Discussion Papers 49/2018, Deutsche Bundesbank.
    4. Wang, Ze & Gao, Xiangyun & Huang, Shupei & Sun, Qingru & Chen, Zhihua & Tang, Renwu & Di, Zengru, 2022. "Measuring systemic risk contribution of global stock markets: A dynamic tail risk network approach," International Review of Financial Analysis, Elsevier, vol. 84(C).
    5. Pagnottoni, Paolo & Spelta, Alessandro, 2023. "The motifs of risk transmission in multivariate time series: Application to commodity prices," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    6. Kick, Thomas & Koetter, Michael & Storz, Manuela, 2020. "Cross-border transmission of emergency liquidity," Journal of Banking & Finance, Elsevier, vol. 113(C).
    7. Pagnottoni, Paolo, 2023. "Superhighways and roads of multivariate time series shock transmission: Application to cryptocurrency, carbon emission and energy prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    8. Xiaoling Tan & Jichang Zhao, 2020. "The illiquidity network of stocks in China's market crash," Papers 2004.01917, arXiv.org, revised Nov 2021.
    9. Colonnello, Stefano & Koetter, Michael & Wagner, Konstantin, 2023. "Compensation regulation in banking: Executive director behavior and bank performance after the EU bonus cap," Journal of Accounting and Economics, Elsevier, vol. 76(1).

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