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arXiv · 2407.18583

CVA Sensitivities, Hedging and Risk

Stéphane Crépey·Botao Li·Hoang Nguyen·Bouazza Saadeddine
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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.

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Stéphane Crépey, Botao Li, Hoang Nguyen, Bouazza Saadeddine. 2024-07-26. CVA Sensitivities, Hedging and Risk. https://arxiv.org/abs/2407.18583

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