Deep xVA solver - A neural network based counterparty credit risk management framework

Alessandro Gnoatto, Athena Picarelli () and Christoph Reisinger ()
Additional contact information
Athena Picarelli: Department of Economics (University of Verona)
Christoph Reisinger: University of Oxford

No 07/2020, Working Papers from University of Verona, Department of Economics

Abstract: In this paper, we present a novel computational framework for portfolio-wide risk management problems where the presence of a potentially large number of risk factors makes traditional numerical techniques ineffective. The new method utilises a coupled system of BSDEs for the valuation adjustments (xVA) and solves these by a recursive application of a neural network based BSDE solver. This not only makes the computation of xVA for high-dimensional problems feasible, but also produces hedge ratios and dynamic risk measures for xVA, and allows simulations of the collateral account.

Keywords: CVA; DVA; FVA; ColVA; xVA; EPE; Collateral; xVA hedging; Deep BSDE Solver (search for similar items in EconPapers)
JEL-codes: C63 G12 G13 (search for similar items in EconPapers)
Pages: 25
Date: 2020-05
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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Related works:
Working Paper: Deep xVA solver -- A neural network based counterparty credit risk management framework (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:ver:wpaper:07/2020

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