We use a reinforcement model to compute the hedging policy for Credit Valuation Adjustment ( CVA ) problems. Reinforcement learning can be used to solve financial applications ofintertemporal choice. In finance, common problems of this kind include pricing and hedging ofcontingent claims,...

We address the problem of how to optimally hedge an options book in a practical setting, where trading decisions are discrete and trading costs can be nonlinear and difficult to model. Based on reinforcement learning, a well-established machine learning technique, our model is shown to be...

The authors propose models for the solution of the fundamental problem of option replication subject to discrete trading, round lotting and nonlinear transaction costs using state-of-the-art methods in deep reinforcement learning (DRL), including deep Q-learning, deep Q-learning with Pop-Art and...