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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 advancements in breaks detection for IVTS to the crucial environment of financial time series. This paper contributes to fill this gap. It employs the Atheoretical Regression Trees framework – a very recent tool that is able to automatically locale multiple breaks occurring to unknown dates – to stock prices. Such a procedure is able to estimate in an efficient way the structural breaks of the considered series; at the same time, it keeps into account the main characteristics of the intervals describing the IVTS. For our purposes, we adopt a theoretical proposal of reading daily stock prices as intervals whose bounds are defined through the closing prices. Empirical experiments on the American International Group – whose daily prices have experienced structural breaks in the past – validate the theoretical model and show the usefulness of the proposed procedure.

The paper introduces a new technique for growing trees for ordinal responses in the model-based framework. We consider the class of cub mixtures which is particularly appropriate to model perceptions and evaluations, as it designs the response process as the combination of a personal feeling and an inherent uncertainty. In our proposal, the partitioning process is based on the local estimation of cub regression models to profile responses according to feeling and uncertainty conditional to the splitting variables. In this regard, two alternative splitting criteria are introduced featuring both inferential and fitting issues. Moreover, the chosen modelling framework allows for advantageous visualization of the classification results. The proposal is illustrated using real data from a survey conducted by the Italian National Statistical Office, with focus on perceived trust towards the European Parliament. A benchmark study is conducted to settle the proposal among the available tree methods for ordinal responses.

In this article we present a computationally efficient method for finding multiple structural breaks at unknown dates based on regression trees. We outline the procedure and present the results of a simulation study to assess the performance of the method and to compare it with the procedure proposed by Bai and Perron. We find the tree-based method performs well in long series which are impractical to analyze with current methods. We apply these methods plus the CUSUM test to the market share of Crest toothpaste between 1958 and 1963. © Taylor & Francis Group, LLC.

This paper addresses the topic of classifying financial time series in a fuzzy framework proposing two fuzzy clustering models both based on GARCH models. In general clustering of financial time series, due to their peculiar features, needs the definition of suitable distance measures. At this aim, the first fuzzy clustering model exploits the autoregressive representation of GARCH models and employs, in the framework of a partitioning around medoids algorithm, the classical autoregressive metric. The second fuzzy clustering model, also based on partitioning around medoids algorithm, uses the Caiado distance, a Mahalanobis-like distance, based on estimated GARCH parameters and covariances that takes into account the information about the volatility structure of time series. In order to illustrate the merits of the proposed fuzzy approaches an application to the problem of classifying 29 time series of Euro exchange rates against international currencies is presented and discussed, also comparing the fuzzy models with their crisp version. © 2012 Elsevier B.V. All rights reserved.

HIV-positive women with pelvic inflammatory disease have been reported to have an increased prevalence of tuboovarian masses (TOMs). The aim of this study was to assess the prevalence of asymptomatic ultrasonographic TOMs in women with HIV and to identify associated factors in order to formulate a selective ultrasonographic screening strategy. Two-hundred and four HIV outpatients underwent transvaginal ultrasonography. Eight (3.9%) had a diagnosis of TOM (5 were asymptomatic). Two profiles of patients at risk for TOM were identified who could be considered for selective screening strategies: the ‘long-term infected’ (age>35 years, diagnosis of HIV infection more than 5 years ago, HIV clinical category C, CD4 counts below 200/mm3, >5 lifetime partners and on antiretroviral therapy) and the ‘recently diagnosed with HIV’ (African ethnicity, age 25–35, HIV diagnosis in the previous year, >5 lifetime partners, HIV clinical category C and not on antiretroviral therapy).