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Atheoretical Regression Trees for classifying risky financial institutions

Author

Listed:
  • Carmela Cappelli

    (Università Federico II di Napoli)

  • Francesca Iorio

    (Università Federico II di Napoli)

  • Angela Maddaloni

    (European Central Bank)

  • Pierpaolo D’Urso

    (Sapienza Università di Roma)

Abstract

We propose a recursive partitioning approach to identify groups of risky financial institutions using a synthetic indicator built on the information arising from a sample of pooled systemic risk measures. The composition and amplitude of the risky groups change over time, emphasizing the periods of high systemic risk stress. We also calculate the probability that a financial institution can change risk group over the next month and show that a firm belonging to the lowest or highest risk group has in general a high probability to remain in that group.

Suggested Citation

  • Carmela Cappelli & Francesca Iorio & Angela Maddaloni & Pierpaolo D’Urso, 2021. "Atheoretical Regression Trees for classifying risky financial institutions," Annals of Operations Research, Springer, vol. 299(1), pages 1357-1377, April.
    • Handle: RePEc:spr:annopr:v:299:y:2021:i:1:d:10.1007_s10479-019-03406-9
    DOI: 10.1007/s10479-019-03406-9
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    References listed on IDEAS

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    Cited by:

    1. Matteo Farnè & Angelos T. Vouldis, 2021. "Banks’ business models in the euro area: a cluster analysis in high dimensions," Annals of Operations Research, Springer, vol. 305(1), pages 23-57, October.

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