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

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