论文标题
选项对冲风险避开加固学习
Option Hedging with Risk Averse Reinforcement Learning
论文作者
论文摘要
在本文中，我们展示了如何使用规避风险的增强学习来对冲选项。我们将最新的规避风险算法应用：信任区域波动率优化（TRVO）对套合套期对冲环境，考虑到诸如离散时间和交易成本之类的现实因素。现实主义使问题双重：代理必须既可以最大程度地减少波动率并包含交易成本，这些任务通常是在竞争中。我们使用该算法来训练各种特征的代理，以不同的风险规避，以便能够跨越波动率-P \＆l空间的有效边界。结果表明，派生的对冲策略不仅优于黑色\＆Scholes delta对冲，而且非常强大和灵活，因为它可以有效地具有不同特征的对冲选项，并且在训练中使用的行为不同。
In this paper we show how risk-averse reinforcement learning can be used to hedge options. We apply a state-of-the-art risk-averse algorithm: Trust Region Volatility Optimization (TRVO) to a vanilla option hedging environment, considering realistic factors such as discrete time and transaction costs. Realism makes the problem twofold: the agent must both minimize volatility and contain transaction costs, these tasks usually being in competition. We use the algorithm to train a sheaf of agents each characterized by a different risk aversion, so to be able to span an efficient frontier on the volatility-p\&l space. The results show that the derived hedging strategy not only outperforms the Black \& Scholes delta hedge, but is also extremely robust and flexible, as it can efficiently hedge options with different characteristics and work on markets with different behaviors than what was used in training.
