Computing XVA for American basket derivatives by Machine Learning techniques

Ludovic Goudenège (), Andrea Molent and Antonino Zanette
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Ludovic Goudenège: Fédération de Mathématiques de CentraleSupélec - CentraleSupélec - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique
Andrea Molent: Università degli Studi di Udine - University of Udine [Italie]
Antonino Zanette: Università degli Studi di Udine - University of Udine [Italie]

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Abstract: Total value adjustment (XVA) is the change in value to be added to the price of a derivative to account for the bilateral default risk and the funding costs. In this paper, we compute such a premium for American basket derivatives whose payoff depends on multiple underlyings. In particular, in our model, those underlying are supposed to follow the multidimensional Black-Scholes stochastic model. In order to determine the XVA, we follow the approach introduced by Burgard and Kjaer \cite{burgard2010pde} and afterward applied by Arregui et al. \cite{arregui2017pde,arregui2019monte} for the one-dimensional American derivatives. The evaluation of the XVA for basket derivatives is particularly challenging as the presence of several underlings leads to a high-dimensional control problem. We tackle such an obstacle by resorting to Gaussian Process Regression, a machine learning technique that allows one to address the curse of dimensionality effectively. Moreover, the use of numerical techniques, such as control variates, turns out to be a powerful tool to improve the accuracy of the proposed methods. The paper includes the results of several numerical experiments that confirm the goodness of the proposed methodologies.

Keywords: Hedging; Greeks; Transaction costs; American options; Heston model; XVA Gaussian Process Regression Basket option Control variates; XVA; Gaussian Process Regression; Basket option; Control variates (search for similar items in EconPapers)
Date: 2022-09-14
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Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:hal-04308564

DOI: 10.48550/arXiv.2209.06485

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