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CVA Sensitivities, Hedging and Risk

Author

Listed:
  • 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)

  • 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

  • Stéphane Crépey & Botao Li & Hoang Nguyen & Bouazza Saadeddine, 2024. "CVA Sensitivities, Hedging and Risk," Working Papers hal-04661959, HAL.
    • Handle: RePEc:hal:wpaper:hal-04661959
    Note: View the original document on HAL open archive server: https://hal.science/hal-04661959v1
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