Efficient Wrong-Way Risk Modelling for Funding Valuation Adjustments

TL;DR

This paper introduces an efficient approximation method for modeling Wrong-Way Risk in Funding Valuation Adjustments, enabling practical inclusion of WWR effects without extensive Monte Carlo simulations.

Contribution

It proposes a Gaussian approximation approach within the affine framework to incorporate WWR into FVA calculations efficiently and robustly.

Findings

The approximation method is applicable to interest rate swaps and multi-currency portfolios.

The approach provides accurate WWR sensitivities for risk management.

Case studies demonstrate the method's practicality and effectiveness.

Abstract

Wrong-Way Risk (WWR) is an important component in Funding Valuation Adjustment (FVA) modelling. Yet, the standard assumption is independence between market risks and the counterparty defaults and funding costs. This typical industrial setting is our point of departure, where we aim to assess the impact of WWR without running a full Monte Carlo simulation with all credit and funding processes. We propose to split the exposure profile into two parts: an independent and a WWR-driven part. For the former, exposures can be re-used from the standard xVA calculation. We express the second part of the exposure profile in terms of the stochastic drivers and approximate these by a common Gaussian stochastic factor. Within the affine setting, the proposed approximation is generic, is an add-on to the existing xVA calculations and provides an efficient and robust way to include WWR in FVA modelling. Case studies for an interest rate swap and a representative multi-currency portfolio of swaps illustrate that the approximation method is applicable in a practical setting. We analyze the approximation error and use the approximation to compute WWR sensitivities, which are needed for risk management. The approach is equally applicable to other metrics such as Credit Valuation Adjustment.