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This work studies the dynamic risk management of the risk-neutral value ofthe potential credit losses on a portfolio of derivatives. Sensitivities-basedhedging of such liability is sub-optimal because of bid-ask costs, pricingmodels which cannot be completely realistic, and a discontinuity at defaulttime. We leverage recent advances on risk-averse Reinforcement Learningdeveloped specifically for option hedging with an ad hoc practice-alignedobjective function aware of pathwise volatility, generalizing them tostochastic horizons. We formalize accurately the evolution of the hedger'sportfolio stressing such aspects. We showcase the efficacy of our approach by anumerical study for a portfolio composed of a single FX forward contract.

CVA Hedging by Risk-Averse Stochastic-Horizon Reinforcement Learning