Michael Jacobs Jr.1
Pricewaterhouse Cooper Advisory LLP
Modern credit risk modeling (e.g., Merton 1974) increasingly relies on advanced mathematical, statistical, and numerical techniques to measure and manage risk in credit portfolios. This gives rise to model risk (OCC/BOG-FRB 2011) and the possibility of understating inherent dangers stemming from very rare yet plausible occurrences not in our reference data sets. In the wake of the financial crisis (Demirguc-Kunt, Detragiache, and Merrouche 2010; Acharya and Schnabl 2009), international supervisors have recognized the importance of stress testing (ST), especially in the realm of credit risk, as can be seen in the revised Basel framework (Basel Committee on Banking Supervision 2005; 2006; 2009a–e; 2010a; 2010b) and the Federal Reserve's Comprehensive Capital Analysis and Review (CCAR) program. It can be and has been argued that the art and science of stress testing has lagged in the domain of credit, as opposed to other types of risk (e.g., market risk), and our objective is to help fill this vacuum. We aim to present classifications and established techniques that will help practitioners formulate robust credit risk stress tests.
We have approached the topic of ST from the point of view of a typical credit portfolio, such as one managed by a typical medium- or large-sized commercial bank. We take this point ...