We present a framework for hedging a portfolio of derivatives in the presence
of market frictions such as transaction costs, market impact, liquidity
constraints or risk limits using modern deep reinforcement machine learning
methods.
We discuss how standard reinforcement learning methods can be applied to
non-linear reward structures, i.e. in our case convex risk measures. As a
general contribution to the use of deep learning for stochastic processes, we
also show that the set of constrained trading strategies used by our algorithm
is large enough to $\epsilon$-approximate any optimal solution.
Our algorithm can be implemented efficiently even in high-dimensional
situations using modern machine learning tools. Its structure does not depend
on specific market dynamics, and generalizes across hedging instruments
including the use of liquid derivatives. Its computational performance is
largely invariant in the size of the portfolio as it depends mainly on the
number of hedging instruments available.
We illustrate our approach by showing the effect on hedging under transaction
costs in a synthetic market driven by the Heston model, where we outperform the
standard "complete market" solution.

本论文提出了一个通过深度强化学习方法，对具有市场摩擦因素（如交易成本、市场反应、流动性约束或风险限制）的衍生产品组合进行避险的框架，在非线性收益结构（如凸风险度量）中应用标准强化学习方法的详细讨论。