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Model and estimation risk in credit risk stress tests, Working Paper, this version April 2015, by Peter Grundke, Kamil Pliszka and Michael Tuchscherer


Stress testing is often performed in a model-based (implicit) way, i.e. adverse realizations of risk factors (e.g., macroeconomic factors) derived from a specific scenario need to be translated with the help of a quantitative model into adverse risk parameter realizations (e.g., default probabilities, default correlations). Usually, these are the same models that are also employed by banks for Pillar 2 risk coverage calculations. In this paper, we focus on credit risk and show how exploiting leeway when setting up and implementing the underlying model can drive the results of a quantitative stress test for default probabilities. For this purpose, we employ several variations of a CreditPortfolioView-style model (including topical approaches like Bayesian model averaging). Our findings show that seemingly only slightly differing specifications can lead to entirely different stress test results. This emphasizes the importance of extensive robustness checks.

Keywords: credit risk; default probability; estimation risk; model risk; stress tests

 JEL classification: G21; G28; G32