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.
Type de document :
Pré-publication, Document de travail
2018
Liste complète des métadonnées

Littérature citée [54 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-01714747
Contributeur : Babacar Diallo <>
Soumis le : mercredi 21 février 2018 - 19:48:33
Dernière modification le : vendredi 4 janvier 2019 - 17:32:34
Document(s) archivé(s) le : mardi 22 mai 2018 - 15:05:25

Fichier

XVA-NMC-GPU-REVISED.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01714747, version 1

Citation

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

Partager

Métriques

Consultations de la notice

453

Téléchargements de fichiers

874