Atheoretical Regression Trees for classifying risky financial institutions

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 This paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed here are those of the authors and do not necessarily reflect those of the ECB or the Eurosystem.