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Proving prediction prudence

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  • Dirk Tasche

Abstract

We study how to perform tests on samples of pairs of observations and predictions in order to assess whether or not the predictions are prudent. Prudence requires that that the mean of the difference of the observation-prediction pairs can be shown to be significantly negative. For safe conclusions, we suggest testing both unweighted (or equally weighted) and weighted means and explicitly taking into account the randomness of individual pairs. The test methods presented are mainly specified as bootstrap and normal approximation algorithms. The tests are general but can be applied in particular in the area of credit risk, both for regulatory and accounting purposes.

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  • Dirk Tasche, 2020. "Proving prediction prudence," Papers 2005.03698, arXiv.org, revised Sep 2022.
    • Handle: RePEc:arx:papers:2005.03698
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    1. Gürtler, Marc & Hibbeln, Martin Thomas & Usselmann, Piet, 2018. "Exposure at default modeling – A theoretical and empirical assessment of estimation approaches and parameter choice," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 176-188.
    2. Hannes Kazianka, 2016. "Objective Bayesian estimation of the probability of default," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(1), pages 1-27, January.
    3. Alexander Shapiro & Jos Berge, 2002. "Statistical inference of minimum rank factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 67(1), pages 79-94, March.
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