CVA Hedging by Risk-Averse Stochastic-Horizon Reinforcement Learning

Roberto Daluiso, Marco Pinciroli, Michele Trapletti and Edoardo Vittori

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Abstract: 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. We showcase the efficacy of our approach by a numerical study for a portfolio composed of a single FX forward contract.

Date: 2023-12
New Economics Papers: this item is included in nep-cmp, nep-rmg and nep-upt
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