Better hedging of CVA with reinforcement learning
This thesis investigates the application of reinforcement learning (RL) to the problem of hedging Credit Valuation Adjustment (CVA), a key component of counterparty credit risk in over-the-counter (OTC) derivatives. While prior studies have demonstrated the effectiveness of RL in hedging simple financial instruments, this work extends the literature by exploring whether RL, specifically the Proxim
