Forecasting bank loans loss-given-default

João Bastos

No 901, CEMAPRE Working Papers from Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon

Abstract: With the advent of the new Basel Capital Accord, banking organizations are invited to estimate credit risk capital requirements using an internal ratings based approach. In order to be compliant with this approach, institutions must estimate the expected loss-given-default, the fraction of the credit exposure that is lost if the borrower defaults. This study evaluates the ability of a parametric fractional response regression and a nonparametric regression tree model to forecast bank loan credit losses. The out-of-sample predictive ability of these models is evaluated at several recovery horizons after the default event. The out-of-time predictive ability is also estimated for a recovery horizon of one year. The performance of the models is benchmarked against recovery estimates given by historical averages. The results suggest that regression trees are an interesting alternative to parametric models in modeling and forecasting loss-given-default.

Keywords: Loss-given-default; Forecasting; Bank loans; Fractional response regression; Regression trees (search for similar items in EconPapers)
JEL-codes: G17 G21 (search for similar items in EconPapers)
Pages: 16 pages
Date: 2009-05
New Economics Papers: this item is included in nep-ban, nep-bec, nep-fmk, nep-for and nep-rmg
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Journal Article: Forecasting bank loans loss-given-default (2010)
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