论文标题
有效的错误道路风险建模用于资助估值调整
Efficient Wrong-Way Risk Modelling for Funding Valuation Adjustments
论文作者
论文摘要
错误路风险（WWR）是资金估值调整（FVA）建模的重要组成部分。但是，标准假设是市场风险与交易对手违约和资金成本之间的独立性。这种典型的工业环境是我们的出发点，我们旨在评估WWR的影响而不运行所有信贷和资金流程的完整蒙特卡洛模拟。我们建议将曝光概况分为两个部分：独立和WWR驱动的部分。对于前者，可以从标准XVA计算中重新使用曝光。我们以随机驱动器的形式表达了暴露概况的第二部分，并通过常见的高斯随机因子近似。在仿射设置中，提出的近似是通用的，是现有XVA计算的附加组件，并提供了一种有效且可靠的方法，可以将WWR包括在FVA建模中。利率互换的案例研究和代表性的多货币掉期组合表明，近似方法适用于实际环境。我们分析了近似错误，并使用近似来计算风险管理所需的WWR敏感性。该方法同样适用于其他指标，例如信用估值调整。
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.
