  A key driver of Credit Value Adjustment (CVA) is the possible dependency
between exposure and counterparty credit risk, known as Wrong-Way Risk (WWR).
At this time, addressing WWR in a both sound and tractable way remains
challenging: arbitrage-free setups have been proposed by academic research
through dynamic models but are computationally intensive and hard to use in
practice. Tractable alternatives based on resampling techniques have been
proposed by the industry, but they lack mathematical foundations. This probably
explains why WWR is not explicitly handled in the Basel III regulatory
framework in spite of its acknowledged importance. The purpose of this paper is
to propose a new method consisting of an appealing compromise: we start from a
stochastic intensity approach and end up with a pricing problem where WWR does
not enter the picture explicitly. This result is achieved thanks to a set of
changes of measure: the WWR effect is now embedded in the drift of the
exposure, and this adjustment can be approximated by a deterministic function
without affecting the level of accuracy typically required for CVA figures. The
performances of our approach are illustrated through an extensive comparison of
Expected Positive Exposure (EPE) profiles and CVA figures produced either by
(i) the standard method relying on a full bivariate Monte Carlo framework and
(ii) our drift-adjustment approximation. Given the uncertainty inherent to CVA,
the proposed method is believed to provide a promising way to handle WWR in a
sound and tractable way.
