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Probability of Default: A Modern Calibration Approach

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Abstract

An extensive academic and practitioner’s literature exists on rating models development with well-structured statistical methods, however these models do not estimate PDs aligned with the economic scenario, then it is necessary a calibration. During the last years the effect of not well calibrated models has been observed on the credit market: actually they show a high level of procyclicality that let them loss market credibility and banking usability. The aim of this paper is to show a modern structured calibration approach, based on Bayesian techniques, taking into consideration specific economic factors. The calibration approach has been applied on real data of a Corporate portfolio of a top tier European Bank and a new calibration test, adjusted by the economic cycle, has been performed.

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Notes

  1. 1.

    \(k^{*}=\phi^{-1}(q)\sqrt{n\mathit{PD}(1-\mathit{PD})+n\mathit{PD}}\)

    where:

    q = is the confidence level of test;

    PD =: is the theoretical PD of each rating grade;

    n=number of observations.

References

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Correspondence to Stefano Bonini .

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Bonini, S., Caivano, G. (2014). Probability of Default: A Modern Calibration Approach. In: Perna, C., Sibillo, M. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer, Cham. https://doi.org/10.1007/978-3-319-05014-0_9

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