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Default probability estimation in small samples--with an application to sovereign bonds

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  • Walter Orth

Abstract

In small samples and especially in the case of small true default probabilities, standard approaches to credit default probability estimation have certain drawbacks. Most importantly, standard estimators display high variability and tend to underestimate the true default probability, which are clearly undesirable properties from the perspective of prudent risk management. As an alternative, we present an empirical Bayes approach to default probability estimation and apply the estimator--which is capable of multi-period predictions--to a comprehensive sample of Standard & Poor's rated sovereign bonds. By means of a simulation study, we then show that the empirical Bayes estimator is more conservative and more precise under realistic data-generating processes.

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  • Walter Orth, 2013. "Default probability estimation in small samples--with an application to sovereign bonds," Quantitative Finance, Taylor & Francis Journals, vol. 13(12), pages 1891-1902, December.
    • Handle: RePEc:taf:quantf:v:13:y:2013:i:12:p:1891-1902
    DOI: 10.1080/14697688.2013.792436
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    References listed on IDEAS

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    1. Fuertes, Ana-Maria & Kalotychou, Elena, 2007. "On sovereign credit migration: A study of alternative estimators and rating dynamics," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3448-3469, April.
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    1. Dalla Valle, Luciana & De Giuli, Maria Elena & Tarantola, Claudia & Manelli, Claudio, 2016. "Default probability estimation via pair copula constructions," European Journal of Operational Research, Elsevier, vol. 249(1), pages 298-311.
    2. Jobst, Rainer & Kellner, Ralf & Rösch, Daniel, 2020. "Bayesian loss given default estimation for European sovereign bonds," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1073-1091.
    3. Oliver Blümke, 2020. "Estimating the probability of default for no‐default and low‐default portfolios," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(1), pages 89-107, January.
    4. Oliver Blümke, 2022. "Multiperiod default probability forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 677-696, July.

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