Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

XVA Principles, Nested Monte Carlo Strategies, and GPU Optimizations

Abstract : We present a nested Monte Carlo (NMC) approach implemented on graphics processing units (GPU) to X-valuation adjustments (XVA), where X ranges over C for credit, F for funding, M for margin, and K for capital. The overall XVA suite involves five compound layers of dependence. Higher layers are launched first and trigger nested simulations on-the-fly whenever required in order to compute an item from a lower layer. If the user is only interested in some of the XVA components, then only the sub-tree corresponding to the most outer XVA needs be processed computationally. Inner layers only need a square root number of simulation with respect to the most outer layer. Some of the layers exhibit a smaller variance. As a result, with GPUs at least, error controlled NMC XVA computations are doable. But, although NMC is naively suited to parallelization, a GPU implementation of NMC XVA computations requires various optimizations. This is illustrated on XVA computations involving equities, interest rate, and credit derivatives, for both bilateral and central clearing XVA metrics.
Document type :
Preprints, Working Papers, ...
Complete list of metadatas

Cited literature [54 references]  Display  Hide  Download
Contributor : Babacar Diallo <>
Submitted on : Wednesday, February 21, 2018 - 7:48:33 PM
Last modification on : Friday, March 27, 2020 - 3:31:18 AM
Document(s) archivé(s) le : Tuesday, May 22, 2018 - 3:05:25 PM


Files produced by the author(s)


  • HAL Id : hal-01714747, version 1


Lokman Abbas-Turki, Stéphane Crépey, Babacar Diallo. XVA Principles, Nested Monte Carlo Strategies, and GPU Optimizations. 2018. ⟨hal-01714747⟩



Record views


Files downloads