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Network linkages to predict bank distress

Author

Listed:
  • Peltonen, Tuomas A.
  • Sarlin, Peter
  • Piloiu, Andreea

Abstract

Building on the literature on systemic risk and financial contagion, the paper introduces estimated network linkages into an early-warning model to predict bank distress among European banks. We use multivariate extreme value theory to estimate equity-based tail-dependence networks, whose links proxy for the markets' view of bank interconnectedness in case of elevated financial stress. The paper finds that early warning models including estimated tail dependencies consistently outperform bank-specific benchmark models with- out networks. The results are robust to variation in model specification and also hold in relation to simpler benchmarks of contagion. Generally, this paper gives direct support for measures of interconnectedness in early-warning models, and moves toward a unified representation of cyclical and cross-sectional dimensions of systemic risk. JEL Classification: G21, G33, C54, D85

Suggested Citation

  • Peltonen, Tuomas A. & Sarlin, Peter & Piloiu, Andreea, 2015. "Network linkages to predict bank distress," Working Paper Series 1828, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20151828
    Note: 355041
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    File URL: https://www.ecb.europa.eu//pub/pdf/scpwps/ecbwp1828.en.pdf
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    References listed on IDEAS

    as
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    Citations

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    Cited by:

    1. Bräuning, Michael & Malikkidou, Despo & Scricco, Giorgio & Scalone, Stefano, 2019. "A new approach to Early Warning Systems for small European banks," Working Paper Series 2348, European Central Bank.
    2. Lang, Jan Hannes & Peltonen, Tuomas A. & Sarlin, Peter, 2018. "A framework for early-warning modeling with an application to banks," Working Paper Series 2182, European Central Bank.
    3. Fendel Ralf & Stremmel Hanno, 2016. "Characteristics of Banking Crises: A Comparative Study with Geographical Contagion," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 236(3), pages 349-388, May.
    4. Geraci, Marco Valerio & Gnabo, Jean-Yves, 2018. "Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying Vector Autoregressions," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(3), pages 1371-1390, June.
    5. J'ozsef Mezei & Peter Sarlin, 2016. "RiskRank: Measuring interconnected risk," Papers 1601.06204, arXiv.org.
    6. Samuel Ronnqvist & Peter Sarlin, 2015. "Detect & Describe: Deep learning of bank stress in the news," Papers 1507.07870, arXiv.org.
    7. Nicola, Giancarlo & Cerchiello, Paola & Aste, Tomaso, 2020. "Information network modeling for U.S. banking systemic risk," LSE Research Online Documents on Economics 107563, London School of Economics and Political Science, LSE Library.
    8. Samuel Ronnqvist & Peter Sarlin, 2016. "Bank distress in the news: Describing events through deep learning," Papers 1603.05670, arXiv.org, revised Dec 2016.
    9. Yulita Wulandari & Musdholifah & Suhal Kusairi, 2017. "The Impact of Macroeconomic and Internal Factors on Banking Distress," International Journal of Economics and Financial Issues, Econjournals, vol. 7(3), pages 429-436.
    10. Marco Valerio Geraci & Jean-Yves Gnabo, 2015. "Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying VARS," Working Papers ECARES ECARES 2015-51, ULB -- Universite Libre de Bruxelles.

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    More about this item

    Keywords

    bank distress; bank networks; systemic risk;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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