  A positive correlation between exposure and counterparty credit risk gives
rise to the so-called Wrong-Way Risk (WWR). Even after a decade of the
financial crisis, addressing WWR in both sound and tractable ways remains
challenging. Academicians have proposed arbitrage-free set-ups through copula
methods but those are computationally expensive and hard to use in practice.
Resampling methods are proposed by the industry but they lack mathematical
foundations. The purpose of this article is to bridge this gap between the
approaches used by academicians and industry. To this end, we propose a
stochastic correlation approach to asses WWR. The methods based on constant
correlation to model the dependency between exposure and counterparty credit
risk assume a linear dependency, thus fail to capture the tail dependence.
Using a stochastic correlation we move further away from the Gaussian copula
and can capture the tail risk. This effect is reflected in the results where
the impact of stochastic correlation on calculated CVA is substantial when
compared to the case when a high constant correlation is assumed between
exposure and credit. Given the uncertainty inherent to CVA, the proposed method
is believed to provide a promising way to model WWR.
