Atheoretical Regression Trees for classifying risky financial institutions

Detecting Multiple Mean Breaks At Unknown Points With Atheoretical Regression Trees

In this paper we propose a computationally effective approach to detect multiple structural breaks in the mean occurring at unknown dates. We propose a non-parametric approach that exploits, in the framework of least squares regression trees, the contiguity property of the Fisher grouping method (1958) proposed for grouping a single real variable. The proposed approach is applied to study the p...

Classifying densities using functional regression trees: Applications in oceanology

The problem of building a regression tree is considered when the response variable is a probability density function. Splitting criteria which are well adapted to measure the dissimilarity between densities are proposed using the Csiszár’s f-divergence. The comparison between performances of trees constructed with various criteria is tackled through numerical simulations. Afterwards, a tree is ...

Risk Measurement for Financial Institutions

International Institutions for Reducing Global Financial Instability

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Institutions and Policies for Maintaining Financial Stability

When journalists from other countries would ask me in the 1980s how the United States could suffer such embarrassingly large losses in its savings and loan, and later its banking, industry, I would be somewhat embarrassed myself, as an American citizen, trying to explain how things could go so wrong in such a developed country. A decade later, the tables seemed to have turned. Many countries ha...