Proving prediction prudence

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 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.

Keywords

• paired difference test
• weighted mean
• credit risk
• probability of default (pd)
• loss given default (lgd)
• exposure at default (ead)
• credit conversion factor (ccf)