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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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https://hal.archives-ouvertes.fr/hal-01714747
Contributor : Babacar Diallo <>
Submitted on : Wednesday, February 21, 2018 - 7:48:33 PM
Last modification on : Friday, March 27, 2020 - 3:31:18 AM
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  • HAL Id : hal-01714747, version 1

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

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