CVA Sensitivities, Hedging and Risk
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866917734481133568 |
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| author | Crépey, Stéphane Li, Botao Nguyen, Hoang Saadeddine, Bouazza |
| author_facet | Crépey, Stéphane Li, Botao Nguyen, Hoang Saadeddine, Bouazza |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18583 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CVA Sensitivities, Hedging and Risk Crépey, Stéphane Li, Botao Nguyen, Hoang Saadeddine, Bouazza Computational Finance 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. |
| title | CVA Sensitivities, Hedging and Risk |
| topic | Computational Finance |
| url | https://arxiv.org/abs/2407.18583 |