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Network linkages to predict bank distress

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  • Constantin, Andreea
  • Peltonen, Tuomas A.
  • Sarlin, Peter

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 without 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.

Suggested Citation

  • Constantin, Andreea & Peltonen, Tuomas A. & Sarlin, Peter, 2018. "Network linkages to predict bank distress," Journal of Financial Stability, Elsevier, vol. 35(C), pages 226-241.
  • Handle: RePEc:eee:finsta:v:35:y:2018:i:c:p:226-241
    DOI: 10.1016/j.jfs.2016.10.011
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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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