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Atheoretical Regression Trees for classifying risky financial institutions

  • S.I.: Recent Developments in Financial Modeling and Risk Management
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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.

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Notes

  1. We thank Federico Nucera for sharing with us the original dataset on which the paper Nucera et al. (2016) is based. We restrict our sample to include only EU institutions and we exclude real estate and other non financial corporation.

  2. http://vlab.stern.nyu.edu.

  3. http://www.esrb.europa.eu/pub/rd/html/index.en.html.

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Authors and Affiliations

  1. Dipartimento di Scienze Politiche, Università Federico II di Napoli, Via L. Rodinò n. 22, 80138, Naples, Italy

    Carmela Cappelli & Francesca Di Iorio

  2. European Central Bank, Sonnemannstrasse 20, 60314 , Frankfurt am Main, Germany

    Angela Maddaloni

  3. Dipartimento di Scienze Sociali ed Economiche, Sapienza Università di Roma, P.le Aldo Moro 5, 00185, Rome, Italy

    Pierpaolo D’Urso

Authors
  1. Carmela Cappelli
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  2. Francesca Di Iorio
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  3. Angela Maddaloni
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  4. Pierpaolo D’Urso
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Correspondence to Carmela Cappelli.

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Appendix

Appendix

Table 3 collects the descriptive statistics of the risk indicators. Table 4 refers to the mean and the standard error of the principal component scores for each risk group and month. Figure 9 describes the characteristics of the class size distribution for each year, while the behavior of the cut-off points over the time period are reported in Fig. 10 (Table 5).

Table 3 Systemic risk indicators descriptive statistics
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Table 4 Mean value in recursive partition (standard error in italic)
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Fig. 9
figure 9

Box plots of monthly risk class sizes by year

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Fig. 10
figure 10

Monthly cut-off points, 2010–2015

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Table 5 Outliers by year in brackets the number of months out of range
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Cappelli, C., Di Iorio, F., Maddaloni, A. et al. Atheoretical Regression Trees for classifying risky financial institutions. Ann Oper Res 299, 1357–1377 (2021). https://doi.org/10.1007/s10479-019-03406-9

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  • DOI: https://doi.org/10.1007/s10479-019-03406-9

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