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CVA Sensitivities, Hedging and Risk

Author

Listed:
  • St'ephane Cr'epey

    (UFR Math\'ematiques UPCit\'e)

  • Botao Li

    (LPSM)

  • Hoang Nguyen

    (IES, LPSM)

  • Bouazza Saadeddine

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.

Suggested Citation

  • St'ephane Cr'epey & Botao Li & Hoang Nguyen & Bouazza Saadeddine, 2024. "CVA Sensitivities, Hedging and Risk," Papers 2407.18583, arXiv.org.
    • Handle: RePEc:arx:papers:2407.18583
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    References listed on IDEAS

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    1. Lokman A. Abbas‐Turki & Stéphane Crépey & Bouazza Saadeddine, 2023. "Pathwise CVA regressions with oversimulated defaults," Mathematical Finance, Wiley Blackwell, vol. 33(2), pages 274-307, April.
    2. Claudio Albanese & Stéphane Crépey & Rodney Hoskinson & Bouazza Saadeddine, 2021. "XVA analysis from the balance sheet," Quantitative Finance, Taylor & Francis Journals, vol. 21(1), pages 99-123, January.
    3. Lokman A Abbas-Turki & Stéphane Crépey & Bouazza Saadeddine, 2023. "Pathwise CVA Regressions With Oversimulated Defaults," Post-Print hal-03910149, HAL.
    4. Lokman Abbas-Turki & St'ephane Cr'epey & Botao Li & Bouazza Saadeddine, 2024. "An Explicit Scheme for Pathwise XVA Computations," Papers 2401.13314, arXiv.org.
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