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Monetary Policy, Financial Crises, and the Macroeconomy pp 59–78Cite as

Network Effects and Systemic Risk in the Banking Sector

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Abstract

This paper provides a review of recent research on the structure of interbank relations and theoretical models developed to assess the contagious potential of shocks (default of single units) via the interbank network. The empirical literature has established a set of stylized facts that includes a fat-tailed distribution of the number of banks, disassortativity of credit links and a pronounced persistence of existing links over time. These topological features correspond to the existence of money center banks, the importance of relationship banking and the self-organization of the interbank market into a core-periphery structure. Models designed to replicate these topological features exhibit on average more contagious potential than baseline models for the generation of random networks (such as the Erdös-Renyi or preferential attachment mechanisms) that do not share the stylized facts. Combining different channels of contagion such as interbank exposures, portfolio overlaps and common exposure to non-financial borrowers, one typically finds that different contagion channels interact in a distinctly nonlinear way.

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Notes

  1. 1.

    Those features that remain constant over time and across ‘space’, i.e. for data from different countries.

  2. 2.

    This high persistence would be almost completely concealed when only considering high-frequency data as it had sometimes been done in the early literature. The reason is that existing credit lines would simply not be activated on each and every day.

  3. 3.

    It needs to be pointed out, that the CP architecture is based on different characteristics of a network than the class of scale free networks. It is, thus, not clear whether the sets of models defined in this way are mutually exclusive. For instance, since the power law of the degree distribution of scale-free networks gives rise to a number of nodes with much higher degrees than the average, these could be identified as the core of the network. Craig and Von Peter (2014) and Fricke and Lux (2015a) have compared their results from estimating a CP model with Monte Carlo simulations of generating mechanisms for scale-free networks and both conclude that the identified core is likely not a spurious finding from a scale-free network topology.

  4. 4.

    Anand et al. (2015) provide an alternative approach for generating sparse networks. Their algorithm is based upon information-theoretic principles and generates the network with minimum density under certain constraints. They report that their generating mechanism overestimates the effects of contagion.

  5. 5.

    Their result has also been featured in the European Central Bank’s Financial Stability report of 2013 (cf. Montagna et al. 2013).

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Acknowledgements

I am very grateful to Zhenxi Chen, Lutz Honvehlmann, Mattia Montagna and Matthias Raddant for discussions and research assistance in the preparation of this manuscript. The most helpful comments by Frank Heinemann are also gratefully acknowledged. The research reported in this paper has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 612955.

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Authors and Affiliations

  1. Department of Economics, University of Kiel, Kiel, Germany

    Thomas Lux

  2. Banco de España Chair in Computational Economics, University Jaume I, Castellón de la Plana, Spain

    Thomas Lux

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  1. Thomas Lux
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Correspondence to Thomas Lux .

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Editors and Affiliations

  1. Chair of Macroeconomics, Technische Universität Berlin, Berlin, Germany

    Frank Heinemann

  2. Darmstadt Business School, Hochschule Darmstadt, Darmstadt, Germany

    Ulrich Klüh

  3. IMK - Macroeconomic Policy Institute, Hans-Böckler-Foundation, Düsseldorf, Germany

    Sebastian Watzka

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Dedicated to Gerhard Illing on the occasion of his 60th birthday.

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Lux, T. (2017). Network Effects and Systemic Risk in the Banking Sector. In: Heinemann, F., Klüh, U., Watzka, S. (eds) Monetary Policy, Financial Crises, and the Macroeconomy. Springer, Cham. https://doi.org/10.1007/978-3-319-56261-2_4

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