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The art of probability-of-default curve calibration

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  • Dirk Tasche

Abstract

PD 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.

Suggested Citation

  • Dirk Tasche, 2012. "The art of probability-of-default curve calibration," Papers 1212.3716, arXiv.org, revised Nov 2013.
  • Handle: RePEc:arx:papers:1212.3716
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    File URL: http://arxiv.org/pdf/1212.3716
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    References listed on IDEAS

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    1. Dirk Tasche, 2009. "Estimating discriminatory power and PD curves when the number of defaults is small," Papers 0905.3928, arXiv.org, revised Mar 2010.
    2. Dirk Tasche, 2006. "Validation of internal rating systems and PD estimates," Papers physics/0606071, arXiv.org.
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    Cited by:

    1. Dirk Tasche, 2014. "Exact fit of simple finite mixture models," Papers 1406.6038, arXiv.org, revised Jul 2014.

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