CVA Sensitivities, Hedging and Risk

Stéphane Crépey (), Botao Li, Hoang Nguyen and Bouazza Saadeddine ()
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Stéphane Crépey: UFR Mathématiques UPCité - UFR Mathématiques [Sciences] - Université Paris Cité - UPCité - Université Paris Cité
Botao Li: LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité
Hoang Nguyen: LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité

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Abstract: We present a unified framework for computing CVA sensitivities, hedging the CVA, and assessing CVA risk, using probabilistic machine learning meant as refined regression tools on simulated data, validatable by low-cost companion Monte Carlo procedures. Various notions of sensitivities are introduced and benchmarked numerically. We identify the sensitivities representing the best practical trade-offs in downstream tasks including CVA hedging and risk assessment.

Keywords: CVA; sensitivities; neural networks; model risk (search for similar items in EconPapers)
Date: 2024-07-26
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