CVA Sensitivities, Hedging and Risk

TL;DR

This paper introduces a unified machine learning framework for computing and hedging CVA sensitivities, enabling efficient risk assessment and improved practical hedging strategies through probabilistic regression on simulated data.

Contribution

It develops a novel probabilistic machine learning approach for CVA sensitivities, integrating benchmarking and practical trade-off analysis for hedging and risk management.

Findings

Identifies optimal sensitivities for CVA hedging.

Demonstrates the effectiveness of machine learning in CVA risk assessment.

Provides benchmark comparisons for various sensitivity notions.

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.