Turbo-charging XVA Greek Calculations
Learn about Numerix’s latest innovation in its XVA engine to support high-speed XVA Greek calculations.
A bump-and-reprice approach has been the mainstream methodology for Greek calculations in risk management for years, due to its intuitive concepts and well-established methodology. However, this approach has its disadvantages, including performance and approximation drawbacks, which become especially acute for XVA Greeks.
To overcome these challenges, an Automatic Differentiation (AD) framework can be employed, as it enables the computation of derivatives with respect to the inputs without re-evaluating the underlying function, and the derivatives produced are very accurate.
Brian Li of Numerix recently showcased how Numerix’s XVA engine uses off-the-shelf AD capabilities from PyTorch to achieve super fast XVA Greek calculations.
In the webinar, Brian covered:
- Overview of the Automatic Differentiation (AD) framework in XVA calculations
- Why Numerix chose PyTorch as a key building block
- Comparisons between AD and bump-and-reprice approaches for XVA Greeks
- Comparisons between AD and re-evaluation for XVA back allocation
- Roll-out timeline
Featured Speakers
Brian Li
Brian Li is Vice President of Financial Engineering at Numerix, focusing on the Oneview for XVA product. Mr. Li previously served as the Vice President of Financial Validation Engineering in the Quality Assurance group at Numerix. He holds an MSc degree in Quantitative Finance from Fordham University, and a BSc degree in Economics from Shanghai Jiaotong University.
Stephanie Chan
Stephanie Chan is a part of the product and field marketing team at Numerix. Her work focuses on developing marketing materials that communicate product features, as well as creating the marketing tools and campaigns to increase awareness of Numerix's products. Prior to joining Numerix in 2019, Ms. Chan held various roles in the corporate communications team at Nomura in Hong Kong and London.
