Modeling Portfolio Risk by Risk Discriminatory Trees and Random Forests

Bill Huajian Yang

MPRA Paper from University Library of Munich, Germany

Abstract: Common tree splitting strategies involve minimizing a criterion function for minimum impurity (i.e. difference) within child nodes. In this paper, we propose an approach based on maximizing a discriminatory criterion for maximum risk difference between child nodes. Maximum discriminatory separation based on risk is expected in credit risk scoring and rating. The search algorithm for an optimal split, proposed in this paper, is efficient and simple, just a scan through the dataset. Choices of different trees, with options either more or less aggressive in variable splitting, are made possible. Two special cases are shown to relate to the Kolmogorov Smirnov (KS) and the intra-cluster correlation (ICC) statistics. As a validation of the proposed approaches, we estimate the exposure at default for a commercial portfolio. Results show, the risk discriminatory trees, constructed and selected using the bagging and random forest, are robust. It is expected that the tools presented in this paper will add value to general portfolio risk modelling.

Keywords: Exposure at default; probability of default; loss given default; discriminatory tree; CART tree; random forest; bagging; KS statistic; intra-cluster correlation; penalty function; risk concordance (search for similar items in EconPapers)
JEL-codes: C1 C14 G32 G38 (search for similar items in EconPapers)
Date: 2013-08-01
New Economics Papers: this item is included in nep-for and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/57245/1/MPRA_paper_57245.pdf original version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:57245

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().