Optimal Right- and Wrong-Way Risk from a Practitioner Standpoint

There are a number of right- and wrong-way-risk methodologies in the literature, but they hardly touch on the most difficult part of those methodologies: model calibration. The authors extend the research on right- and wrong-way-risk methodologies with a comprehensive empirical analysis of the market credit dependency structure. Using 150 case studies, they found evidence of the real market credit dependency structure and produced market-calibrated model parameters. Using these realistic calibrations, they examined right-way and wrong-way risk in both real and fundamental trades by calculating the change in major credit risk metrics that banks use. They show that these metrics can vary significantly, in both the “right” and the “wrong” ways. The authors explain why having a good right- and wrong-way-risk model is important and describe the consequences of not having one.Right-way and wrong-way-risk modeling have received increasing attention in the past few years. A number of models have been proposed. At present, there is no indication in the literature as to which of these proposed models is optimal, and calibration is only loosely touched on. Although the existing literature focuses on credit value adjustment (CVA), other very important credit-driven risk metrics, such as initial margin, exposure management, and regulatory capital, can also be affected by right-way and wrong-way risk. The authors extend the current state-of-the-art research on right- and wrong-way-risk methodologies with a comprehensive empirical analysis of the market credit dependency structure. Using 150 case studies, they provide evidence of the real market credit dependency structure and give market-calibrated model parameters. This article offers the pillars of a stochastic correlation model, driven by empirical data, that could be optimal for pricing and risk management of complex structures. Using these realistic calibrations, the authors carry out an impact study of right-way and wrong-way risk in real trades (in all relevant asset classes) and in fundamental trades by calculating the change in many major credit risk metrics that banks use (CVA, initial margin, exposure measurement, capital) when this risk is taken into account—all of this for both collateralized and uncollateralized trades. The results show that these metrics can vary quite significantly, in both the “right” and the “wrong” ways. Finally, on the basis of this impact study, the authors explain why a good right- and wrong-way-risk model is central to financial institutions and describe the consequences of not having one.