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Interbank Lending and Distress: Observables, Unobservables, and Network Structure

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  • Ben R. Craig
  • Michael Koetter
  • Ulrich Kruger

Abstract

We provide empirical evidence on the relevance of systemic risk through the interbank lending channel. We adapt a spatial probit model that allows for correlated error terms in the cross-sectional variation that depend on the measured network connections of the banks. The latter are in our application observed interbank exposures among German bank holding companies during 2001 and 2006. The results clearly indicate significant spillover effects between banks? probabilities of distress and the financial profiles of connected peers. Better capitalized and managed connections reduce the banks own risk. Higher network centrality reduces the probability of distress, supporting the notion that more complete networks tend to be more stable. Finally, spatial autocorrelation is significant and negative. This last result may indicate too-many-to-fail mechanics such that bank distress is less likely if many peers already experienced distress.

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  • Ben R. Craig & Michael Koetter & Ulrich Kruger, 2014. "Interbank Lending and Distress: Observables, Unobservables, and Network Structure," Working Papers (Old Series) 1418, Federal Reserve Bank of Cleveland.
  • Handle: RePEc:fip:fedcwp:1418
    DOI: 10.26509/frbc-wp-201418
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    Cited by:

    1. Margaretic, Paula & Cifuentes, Rodrigo & Carreño, José Gabriel, 2021. "Banks’ interconnections and peer effects: Evidence from Chile," Research in International Business and Finance, Elsevier, vol. 58(C).
    2. Covi, Giovanni & Gorpe, Mehmet Ziya & Kok, Christoffer, 2021. "CoMap: Mapping Contagion in the Euro Area Banking Sector," Journal of Financial Stability, Elsevier, vol. 53(C).
    3. Fink, Kilian & Krüger, Ulrich & Meller, Barbara & Wong, Lui-Hsian, 2016. "The credit quality channel: Modeling contagion in the interbank market," Journal of Financial Stability, Elsevier, vol. 25(C), pages 83-97.
    4. Ding, Dong & Sickles, Robin C., 2018. "Capital Regulation, Efficiency, and Risk Taking: A Spatial Panel Analysis of U.S. Banks," Working Papers 18-004, Rice University, Department of Economics.
    5. Y'erali Gandica & Sophie B'ereau & Jean-Yves Gnabo, 2019. "A multilevel analysis to systemic exposure: insights from local and system-wide information," Papers 1910.08611, arXiv.org.
    6. Paul Glasserman & Peyton Young, 2015. "Contagion in Financial Networks," Economics Series Working Papers 764, University of Oxford, Department of Economics.
    7. Navarro, Noemí & Tran, Dan H., 2018. "Shock Diffusion in Regular Networks: The Role of Transitive Cycles," MPRA Paper 86267, University Library of Munich, Germany.
    8. Paul Glasserman & H. Peyton Young, 2015. "Contagion in Financial Markets," Working Papers 15-21, Office of Financial Research, US Department of the Treasury.
    9. Francesco Palazzo, 2016. "Peer monitoring via loss mutualization," Temi di discussione (Economic working papers) 1088, Bank of Italy, Economic Research and International Relations Area.

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    More about this item

    Keywords

    Spatial Autoregression; interbank connections; bank risk;
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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