Optimal Right and Wrong Way Risk

by Ignacio Ruiz of iRuiz Consulting,
Ricardo Pachon of Credit Suisse, and
Piero del Boca of Credit Suisse

April 2013

Abstract: Right-way and wrong-way risk modelling, in the context of counterparty credit risk for books of financial derivatives, has gathered increasing attention in the past few years. A number of models have been proposed for it; the core of them goes around how to model the market-credit dependency structure in a computationally effective way. At present, there is no indication in the literature as to which of these proposed models is optimal, and calibration is only loosely touched upon, if at all. This is in contrast to the fact that calibration of those models is the most difficult part of their implementation. Also, while existing papers in the area focus in CVA, other very important credit-driven risk metrics such as initial margin, exposure profiles for exposure management and regulatory capital, both CCR and CVA-VaR, can also notably be effected by right-way and wrong-way risk. In this paper, the authors first explain the underlying source of this risk and how it applies to CVA as well as other credit metrics. They then perform a methodology review of the existing literature, providing at the end of it a critique of the different models and their view as to which is the optimal framework, and why. This is done from the standpoint of a practitioner, with special consideration of practical implementation and utilisation issues. After that, they extend the current state-of-the-art research in the chosen methodology with a comprehensive empirical analysis of the market-credit dependency structure. They utilise 150 case studies, providing evidence of what is the real market-credit dependency structure, and giving calibrated model parameters as of January 2013. Next, using these realistic calibrations, they carry out an impact study of right-way and wrong-way risk in real trades, in all relevant asset classes (equity, FX and commodities) and trade types (swaps, options and futures). This is accomplished by calculating the change in all major credit risk metrics that banks use (CVA, initial margin, exposure measurement and capital) when this risk is taken into account. All this is done both for collateralised and uncollateralised trades. The results show how these credit metrics can vary quite significantly, both in the "right" and the "wrong" ways. This analysis also illustrates the effect of collateral; for example, how a trade can have wrong-way risk when uncollateralised, but right-way risk when collaterallised. Finally, based on this impact study, the authors explain why a good right and wrong way risk model (as opposed to "any" model that gives a result) is central to financial institutions, furthermore describing the consequences of not having one.

JEL Classification: C0, N2.

AMS Classification: 91B25, 91B30, 91G40.

Keywords: Counterparty Credit Risk, Wrong Way Risk, Right Way Risk, CVA, Initial Margin, Exposure Management, Capital Calculation.

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