Fast Sensitivities for FRTB SA-CVA (Capital)

The paper gives an overview of the proposed FRTB SA-CVA framework, discusses the approaches for computing CVA sensitivities, and shows how AAD can be applied

Deficiencies of the Basel III standard approach for CVA capital charge led to fundamental changes in the proposed Fundamental Review of the Trading Book FRTB SA-CVA framework. With this approach, banks have to compute CVA sensitivities to a large number of market risk factors, which are typically in the hundreds or even thousands. The FRTB-CVA document states: “CVA sensitivities to market risk factors are computationally very expensive.” (While this is true for the traditional bump-and-revalue approach (finite differences) as it means hundreds to thousands of revaluations of the entire CVA multi-step Monte-Carlo simulation, Adjoint Algorithmic Differentiation (AAD) provides a way out. With AAD, all sensitivities can be obtained at a small additional computational cost compared to a single valuation.

This paper gives a brief overview of the proposed FRTB SA-CVA framework, discusses the complexities and reviews approaches for computing CVA sensitivities. It shows how AAD can be applied leading to large performance gains and gives practical implementation guidelines.

  • FRTB SA-CVA overview (CVA Capital – BCBS 265)
  • Computing CVA sensitivities
  • Challenges of the multi-step Monte-Carlo
  • Developing clean maintainable AAD code
  • AAD manual implementation and automatic tools
  • Incorporating external libraries
  • Combining AAD and bumping
  • Lowering the memory footprint

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