Ricerc@Sapienza

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.

Multiple structural breaks detection for Interval-Valued Time Series (IVTS) is undoubtedly relevant under
practical perspectives and challenging under the point of view of the analysis of expert systems. In this
respect, financial time series usually show high variability and outliers; moreover, they often exhibit the
property of being of high frequency nature; thus, it is naturally advisable to consider them as IVTS type
for a given time unit. Despite this relevance, scarce effort has been spent by scholars to apply the methodological