A systematic approach to multi-period stress testing of portfolio credit risk

We propose a new method for analysing multiperiod stress scenarios for portfolio credit risk more systematically than in the current practice of macro stress testing. Our method quantifies the plausibility of scenarios by considering the distance of the stress scenario from an average scenario. For a given level of plausibility our method searches systematically for the most adverse scenario for the given portfolio. This method therefore gives a formal criterion for judging the plausibility of scenarios and it makes sure that no plausible scenario will be missed. We show how this method can be applied to a range of models already in use among stress testing practitioners. While worst case search requires numerical optimisation we show that for practically relevant cases we can work with reasonably good linear approximations to the portfolio loss function that make the method computationally very efficient and easy to implement. Applying our approach to data from the Spanish loan register and using a portfolio credit risk model we show that, compared to standard stress test procedures, our method identifies more harmful scenarios that are equally plausible.