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Modeling Portfolio Risk by Risk Discriminatory Trees and Random Forests

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  • Yang, Bill Huajian

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.

Suggested Citation

  • Yang, Bill Huajian, 2013. "Modeling Portfolio Risk by Risk Discriminatory Trees and Random Forests," MPRA Paper 57245, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:57245
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    File URL: https://mpra.ub.uni-muenchen.de/57245/1/MPRA_paper_57245.pdf
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    References listed on IDEAS

    as
    1. Lin, Yi & Jeon, Yongho, 2006. "Random Forests and Adaptive Nearest Neighbors," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 578-590, June.
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    More about this item

    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;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation

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