The art of probability-of-default curve calibration
AbstractPD curve calibration refers to the transformation of a set of rating grade level probabilities of default (PDs) to another average PD level that is determined by a change of the underlying portfolio-wide PD. This paper presents a framework that allows to explore a variety of calibration approaches and the conditions under which they are fit for purpose. We test the approaches discussed by applying them to publicly available datasets of agency rating and default statistics that can be considered typical for the scope of application of the approaches. We show that the popular 'scaled PDs' approach is theoretically questionable and identify an alternative calibration approach ('scaled likelihood ratio') that is both theoretically sound and performs better on the test datasets. Keywords: Probability of default, calibration, likelihood ratio, Bayes' formula, rating profile, binary classification.
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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1212.3716.
Date of creation: Dec 2012
Date of revision: Nov 2013
Publication status: Published in Journal of Credit Risk 9(4), 63-103, 2013
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Web page: http://arxiv.org/
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-01-07 (All new papers)
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- Dirk Tasche, 2014. "Exact fit of simple finite mixture models," Papers 1406.6038, arXiv.org, revised Jul 2014.
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