关于估值调整的带波动微笑的赫尔-怀特模型

T. van der Zwaard, L. A. Grzelak, C. W. Oosterlee
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引用次数: 0

摘要

仿射扩散动力学由于其分析的可操作性,经常被用于估值调整(xVA)计算。然而,这些模型无法捕捉市场暗示的偏斜和微笑,而这在计算 xVA 指标时是相关的。因此,需要额外的自由度来捕捉这些市场特征。在本文中,我们通过一个具有状态依赖系数的 SDE 来解决这个问题。该 SDE 符合有限数量的不同 AD 动态的凸组合。我们结合了赫尔-怀特(Hull-White)单因素模型,其中一个模型参数是可变的。我们使用随机 AD(RAnD)技术对动态组合进行参数化。我们将具有状态相关系数的 SDE 和原始模型的 RAnD 参数化称为 rHW 模型。通过赫尔-怀特动力学的可分析性,rHW 模型可以对欧洲掉期进行高效的半分析校准。我们使用基于回归的蒙特卡罗模拟来计算风险敞口。在这种情况下,我们证明了偏斜和微笑对线性和提前行使利率评级衍生品的风险敞口和 xVAs 的显著影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the Hull-White model with volatility smile for Valuation Adjustments
Affine Diffusion dynamics are frequently used for Valuation Adjustments (xVA) calculations due to their analytic tractability. However, these models cannot capture the market-implied skew and smile, which are relevant when computing xVA metrics. Hence, additional degrees of freedom are required to capture these market features. In this paper, we address this through an SDE with state-dependent coefficients. The SDE is consistent with the convex combination of a finite number of different AD dynamics. We combine Hull-White one-factor models where one model parameter is varied. We use the Randomized AD (RAnD) technique to parameterize the combination of dynamics. We refer to our SDE with state-dependent coefficients and the RAnD parametrization of the original models as the rHW model. The rHW model allows for efficient semi-analytic calibration to European swaptions through the analytic tractability of the Hull-White dynamics. We use a regression-based Monte-Carlo simulation to calculate exposures. In this setting, we demonstrate the significant effect of skew and smile on exposures and xVAs of linear and early-exercise interest rate derivatives.
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