CVA Hedging by Risk-Averse Stochastic-Horizon Reinforcement Learning

Understand

This work studies the dynamic risk management of the risk-neutral value of the potential credit losses on a portfolio of derivatives.

• Sensitivities-based hedging of such liability is sub-optimal because of bid-ask costs, pricing models which cannot be completely realistic, and a discontinuity at default time.
• We leverage recent advances on risk-averse Reinforcement Learning developed specifically for option hedging with an ad hoc practice-aligned objective function aware of pathwise volatility, generalizing them to stochastic horizons.
• We formalize accurately the evolution of the hedger's portfolio stressing such aspects.