CVA Sensitivities, Hedging and Risk

ArXiv ID: 2407.18583 “View on arXiv”

Authors: Unknown

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.

Keywords: CVA (Credit Valuation Adjustment), Probabilistic Machine Learning, Monte Carlo Simulation, Sensitivity Analysis, Hedging, Counterparty Credit Risk / Derivatives

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced probabilistic machine learning, SVD, and neural networks with complex derivations (Malliavin calculus referenced), indicating high mathematical density. It is highly empirical with a provided GitHub code link, explicit backtesting on financial CVA, and implementation on specific hardware (GPU/CPU) with statistical rigor.

  flowchart TD
    A["Research Goal<br>Unified Framework for CVA<br>Sensitivities, Hedging & Risk"] --> B["Methodology<br>Probabilistic Machine Learning<br>Refined Regression + Monte Carlo"]
    B --> C["Inputs<br>Simulated Market & Counterparty Data"]
    C --> D["Computation<br>Calculate CVA & Sensitivities"]
    D --> E["Application<br>Hedging & Risk Assessment"]
    E --> F["Outcomes<br>Identified Practical Trade-offs<br>Validated Framework"]