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.
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Pré-publication, Document de travail
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Contributeur : Babacar Diallo <>
Soumis le : mercredi 21 février 2018 - 19:48:33
Dernière modification le : mardi 19 mars 2019 - 01:18:58
Document(s) archivé(s) le : mardi 22 mai 2018 - 15:05:25


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  • 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〉



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