Validating default models when the validation data are corrupted : Analytic results and bias corrections

Abstract

There has been a growing recognition in industry that issues of data quality, which are routine in practice, can materially affect the assessment of credit model performance. In this paper, we develop some analytic results that are useful in sizing the biases associated with tests of default model power performed using corrupt (“noisy”) data. As it is… (More)

6 Figures and Tables

Topics

  • Presentations referencing similar topics