Atheoretical Regression Trees for classifying risky financial institutions

Carmela Cappelli (), Francesca Di Iorio, Angela Maddaloni and Pierpaolo D’Urso ()
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Carmela Cappelli: Università Federico II di Napoli
Pierpaolo D’Urso: Sapienza Università di Roma

Annals of Operations Research, 2021, vol. 299, issue 1, No 53, 1357-1377

Abstract: 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.

Keywords: Systemic risk; Financial stress; Atheoretical Regression Trees; Factor analysis (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10479-019-03406-9

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