论文标题
深贝尔曼对冲
Deep Bellman Hedging
论文作者
论文摘要
我们提出了一种参与者批判性的增强学习算法，该算法用于解决套期使用纯历史数据的金融工具（例如证券和非处方衍生物）的产品组合的问题。我们方法的关键特征是：具有向前，掉期，未来，期权等衍生品的对冲的能力；纳入交易摩擦，例如交易成本和流动性约束；适用于任何合理的金融工具组合；现实，连续的状态和行动空间；和正式的风险调整后的返回目标。最重要的是，受过训练的模型为任意初始投资组合和市场状态提供了最佳的对冲，而无需重新培训。我们还证明存在有限的解决方案，并显示了与我们的香草深度套期保值方法的关系
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: the 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
