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

Arbitrage-free pricing of CVA for cross-currency swap with wrong-way risk under stochastic correlation modeling framework