Deep Bellman Hedging
17 Pages Posted: 13 Jul 2022 Last revised: 18 Jul 2022
Date Written: June 30, 2022
We present an actor-critic-type reinforcement learning algorithm for solving the problem of hedging a portfolio of financial instruments such as securities and over-the-counter derivatives using purely historic data. The key characteristics of our approach are: he ability to hedge with derivatives such as forwards, swaps, futures, options; incorporation of trading frictions such as trading cost and liquidity constraints; applicability for any reasonable portfolio of financial instruments; realistic, continuous state and action spaces; and formal risk-adjusted return objectives.
Most importantly, the trained model provides an optimal hedge for arbitrary initial portfolios and market states without the need for re-training.
We also prove existence of finite solutions to our Bellman equation, and show the relation to our vanilla Deep Hedging approach
Keywords: Deep Hedging, Reinforcement Learning, Convex Risk Measures, Hedging
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