Developing and maintaining an advanced model is complex and associated with high costs. Especially the simulation of potential market scenarios and the valuation of transactions for each scenario and viewpoint are complex and laborious tasks. An advanced approach provides the most realistic risk assessment, but requires in-depth quantitative knowledge, a multitude of input data and a powerful infrastructure. Using advanced approaches is the most sophisticated way to quantify CCR exposures, and there is plenty of academic literature on their application (see Pykhtin and Zhu 2007 Pricso and Rosen 2005 Picoult 2004 Canabarro and Duffie 2003 Picoult 2004). According to Gregory ( 2015), these methods can be divided into advanced, parametric and semi-analytical approaches. The lack of clear guidance from accountants and supervisors as well as the need for complex and simpler methods have led to the development of a wide range of approaches. Section 6 concludes this paper.Ĭalculation of xVAs requires the quantification of the expected exposure at time t. The methodology and results of the empirical analysis are presented in Sect. The calibration of the modified SA-CCR is lined out in Sect. 3 we derive the necessary adjustments to the SA-CCR based on central model foundations. Section 2 provides an overview and categorization of existing methods for exposure quantification. The remainder of the paper is structured as follows. As we maintain the key building blocks and methodological assumptions of the supervisory SA-CCR, we offer a flexible and consistent approach to calibration based on market-implied volatilities, yet simple enough to be adopted by smaller institutions with limited personal resources. Hence, we are able to mirror exposure profiles generated by advanced methods, which might serve as input for CVA calculation. We find that our modified SA-CCR approach is able to produce expected exposure profiles capturing the main exposure dynamics of interest rate and foreign exchange positions. To ensure the usability of our approach, we compare our results with an advanced model approach for an illustrative set of interest rate and foreign exchange derivatives. Secondly, we show that our approach is applicable to multiple asset classes and on a single-transaction as well as a netting set level. The approach has a flexible structure and is able to capture risk mitigating effects from margining and collateralization. We derive the necessary adjustments to the regulatory SA-CCR in order to ensure consistency with IFRS 13. Firstly, we develop a fast and simple semi-analytical method for exposure calculation, which is a modified version of the new supervisory standardized approach for measuring counterparty credit risk exposures (SA-CCR). Our paper provides the following contributions.
Hence, most of these methods are not applicable to multi-dimensional netting sets. Most existing approaches are either too simplistic to be robust, only applicable on transaction level or suitable for a small range of products.
As many market participants may not be able to apply highly complex and sophisticated methods, there is a need for simpler semi-analytical and parametric approaches. Given the lack of clear methodological guidance in IFRS 13, a wide range of methods has been developed by regulators, financial institutions and scientists alike. The most time-consuming and complex part of xVA calculation is the determination of the expected exposure. Footnote 1 Thirdly, financial institutions are expected to calculate minimum capital requirements for CVA risk under Basel III, which implies the calculation of CVA as well as CVA sensitivities. Secondly, international financial reporting standards (IFRS 13) require all entities involved in derivative transactions to consider CCR in the accounting fair value. Firstly, market participants need to consider CCR when pricing derivatives. Today, the consideration of CCR is market standard and the calculation of credit valuation adjustments (CVA) has evolved to be a fundamental task for entities involved in derivatives trading due to several reasons. The financial crisis and its aftermath have revealed the importance of counterparty credit risk (CCR) in over-the-counter (OTC) derivative transactions.
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