We propose a tree-based approach to identify groups of risky financial institutions. We use a synthetic indicator based on the information arising from a sample of pooled systemic risk measures. The composition and amplitude of the risky groups changes over time, emphasizing the periods of high systemic risk stress. We also calculate the probability that a bank can change risk group over the next month and show that a bank belonging to the lowest or highest risk group has a high probability to remain in that group.
Tree based methods for classifying risky financial institutions / Cappelli, C.; DI IORIO, Francesca; Maddaloni, A.. - (2017). (Intervento presentato al convegno ICEEE-7TH: SEVENTH ITALIAN CONGRESS OF ECONOMETRICS AND EMPIRICAL ECONOMICS tenutosi a Università di Messina nel 25-27 January 2017).
Tree based methods for classifying risky financial institutions
Cappelli C.;DI IORIO, FRANCESCA;
2017
Abstract
We propose a tree-based approach to identify groups of risky financial institutions. We use a synthetic indicator based on the information arising from a sample of pooled systemic risk measures. The composition and amplitude of the risky groups changes over time, emphasizing the periods of high systemic risk stress. We also calculate the probability that a bank can change risk group over the next month and show that a bank belonging to the lowest or highest risk group has a high probability to remain in that group.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
