A deep learning approach for computations of exposure profiles for high-dimensional Bermudan options

Kristoffer Andersson and Cornelis Oosterlee

Applied Mathematics and Computation, 2021, vol. 408, issue C

Abstract: In this paper, we propose a neural network-based method for approximating expected exposures and potential future exposures of Bermudan options. In a first phase, the method relies on the Deep Optimal Stopping algorithm (DOS) proposed by Becker, Cheridito, and Jentzen (2019), which learns the optimal stopping rule from Monte-Carlo samples of the underlying risk factors. Cashflow paths are then created by applying the learned stopping strategy on a new set of realizations of the risk factors. Furthermore, in a second phase the cashflow paths are projected onto the risk factors to obtain approximations of pathwise option values. The regression step is carried out by ordinary least squares as well as neural networks, and it is shown that the latter results in more accurate approximations.

Keywords: Optimal stopping; Deep learning; Expected exposure; Potential future exposure; XVA (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Working Paper: A deep learning approach for computations of exposure profiles for high-dimensional Bermudan options (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:408:y:2021:i:c:s0096300321004215

DOI: 10.1016/j.amc.2021.126332

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