Cao Jay, Chen Jacky, Farghadani Soroush, Hull John, Poulos Zissis, Wang Zeyu, Yuan Jun
Joseph L. Rotman School of Management, University of Toronto, Toronto, ON, Canada.
Department of Computer Science, University of Toronto, Toronto, ON, Canada.
Front Artif Intell. 2023 Feb 22;6:1129370. doi: 10.3389/frai.2023.1129370. eCollection 2023.
We show how reinforcement learning 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 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.
我们展示了如何将强化学习与分位数回归结合使用，为负责随机到达且依赖单一基础资产的衍生品交易员制定套期保值策略。我们假设交易员在每天结束时通过持有基础资产头寸使投资组合的德尔塔中性。我们关注如何利用期权交易来管理伽马和维加。期权交易存在交易成本。我们考虑三种不同的目标函数。我们得出关于最优套期保值策略如何取决于交易员的目标函数、交易成本水平以及用于套期保值的期权到期日的结论。我们还研究了套期保值策略对基础资产假设过程的稳健性。
