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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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.
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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).
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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
- Systemic risk
- Financial stress
- Atheoretical Regression Trees
- Factor analysis