Everything You Always Wanted to Know About XVA Model Risk but Were Afraid to Ask

Lorenzo Silotto, Marco Scaringi, M. Bianchetti
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引用次数: 1

Abstract

Valuation adjustments, collectively named XVA, play an important role in modern derivatives pricing. XVA are an exotic pricing component since they require the forward simulation of multiple risk factors in order to compute the portfolio exposure including collateral, leading to a significant model risk and computational effort, even in case of plain vanilla trades. This work analyses the most critical model risk factors, meant as those to which XVA are most sensitive, finding an acceptable compromise between accuracy and performance. This task has been conducted in a complete context including a market standard multi-curve G2++ model calibrated on real market data, both Variation Margin and ISDA-SIMM dynamic Initial Margin, different collateralization schemes, and the most common linear and non-linear interest rates derivatives. Moreover, we considered an alternative analytical approach for XVA in case of uncollateralized Swaps. We show that a crucial element is the construction of a parsimonious time grid capable of capturing all periodical spikes arising in collateralized exposure during the Margin Period of Risk. To this end, we propose a workaround to efficiently capture all spikes. Moreover, we show that there exists a parameterization which allows to obtain accurate results in a reasonable time, which is a very important feature for practical applications. In order to address the valuation uncertainty linked to the existence of a range of different parameterizations, we calculate the Model Risk AVA (Additional Valuation Adjustment) for XVA according to the provisions of the EU Prudent Valuation regulation. Finally, this work can serve as an handbook containing step-by-step instructions for the implementation of a complete, realistic and robust modelling framework of collateralized exposure and XVA.
关于XVA模型风险你一直想知道却不敢问的一切
估值调整,统称为XVA,在现代衍生品定价中发挥着重要作用。XVA是一个奇特的定价组件,因为它们需要对多个风险因素进行远期模拟,以计算包括抵押品在内的投资组合风险,这导致了重大的模型风险和计算工作量,即使是在普通交易的情况下。这项工作分析了最关键的模型风险因素,即那些XVA最敏感的因素,在准确性和性能之间找到一个可接受的折衷方案。这项任务是在一个完整的背景下进行的,包括一个基于真实市场数据校准的市场标准多曲线G2++模型,变异保证金和ISDA-SIMM动态初始保证金,不同的抵押方案,以及最常见的线性和非线性利率衍生品。此外,我们考虑了在无担保掉期的情况下对XVA的另一种分析方法。我们表明,一个关键因素是构建一个简洁的时间网格,能够捕捉在风险保证金期间在抵押敞口中产生的所有周期性峰值。为此,我们提出了一种有效捕获所有峰值的解决方案。此外,我们还表明存在一种参数化,可以在合理的时间内获得准确的结果,这对于实际应用来说是一个非常重要的特征。为了解决与存在一系列不同参数化相关的估值不确定性,我们根据欧盟审慎估值法规的规定计算了XVA的模型风险AVA(附加估值调整)。最后,这项工作可以作为一本手册,其中包含了一个完整的、现实的、健壮的抵押敞口和XVA建模框架的逐步实施说明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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