An Accurate Solution for Credit Value Adjustment (CVA) and Wrong Way Risk

Abstract

This paper presents a new framework for credit value adjustment (CVA) that is a relatively new area of financial derivative modeling and trading. In contrast to previous studies, the model relies on the probability distribution of a default time rather than the default time itself, as the default time is usually inaccessible. As such, the model can achieve a high order of accuracy and is relatively easy to implement. The model captures wrong or right way risk naturally. Using a unique dataset, we find empirical evidence that wrong or right way risk has a material effect on risky valuation and CVA. The magnitude of the impact is greater in credit and equity markets. The results also indicate that diversification is an effective way to mitigate wrong or right way risk