CVA Sensitivities, Hedging and Risk
Computational Finance
2024-07-29 v1
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.
Cite
@article{arxiv.2407.18583,
title = {CVA Sensitivities, Hedging and Risk},
author = {Stéphane Crépey and Botao Li and Hoang Nguyen and Bouazza Saadeddine},
journal= {arXiv preprint arXiv:2407.18583},
year = {2024}
}
Comments
This is the long, preprint version of the eponymous paper forthcoming in Risk Magazine