Understanding the Behaviour of Credit Correlations Under Stress

Michael Kalkbrener (Deutsche Bank AG)
Thursday, February 7, 2013 - 4:00pm
TU Berlin, Raum MA 041

Understanding the Behaviour of Credit Correlations Under Stress We present a general approach to implementing stress scenarios in a multi-factor credit portfolio model. Although the methodology is developed in a particular factor model, the main concept - stressing risk factors through a truncation of their distributions - is independent of the model specification. We derive analytic formulae for asset correlations under stress in Gaussian and t-distributed factor models. For the more general class of normal variance mixture (NVM) models, we calculate the asymptotic limit of the correlation under stress, which depends on whether the variables are in the maximum domain of attraction of the Frechet or Gumbel distribution. It turns out that correlations in heavy-tailed NVM models are less sensitive to stress than in medium- or light-tailed models. Our analysis sheds light on the suitability of this model class to serve as a quantitative framework for stress testing, and as such provides important information for risk and capital management in financial institutions, where NVM models are frequently used for assessing capital adequacy.