论文标题
使用深厚的分配加固学习，伽玛和Vega对冲
Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning
论文作者
论文摘要
我们展示了如何将D4PG与分位数回归结合使用，以为负责随机到达并取决于单个基础资产的衍生品的交易者制定对冲策略。我们假设交易者在每天结束时通过在基础资产中担任职位来使投资组合三角洲中性。我们专注于如何使用选项中的交易来管理伽玛和Vega。期权交易可能会承担交易成本。我们考虑三个不同的目标功能。我们得出的结论是关于最佳对冲策略如何取决于交易者的目标函数，交易成本水平以及用于对冲的期权的成熟度。我们还调查了对冲策略的鲁棒性，以实现基础资产所假定的过程。
We show how D4PG can be used in conjunction with quantile regression to develop a hedging strategy for a trader responsible for derivatives that arrive stochastically and depend on a single underlying asset. We assume that the trader makes the portfolio delta neutral at the end of each day by taking a position in the underlying asset. We focus on how trades in the options can be used to manage gamma and vega. The option trades are subject to transaction costs. We consider three different objective functions. We reach conclusions on how the optimal hedging strategy depends on the trader's objective function, the level of transaction costs, and the maturity of the options used for hedging. We also investigate the robustness of the hedging strategy to the process assumed for the underlying asset.
