Research in quantitative finance has demonstrated that reinforcement learning (RL) methods have delivered promising outcomes in the context of hedging financial portfolios. For example, hedging a portfolio of European options using RL achieves better $PnL$ distribution than the trading hedging strategies like Delta neutral and Delta-Gamma neutral [Cao et. al. 2020]. There is great attention given to the hedging of vanilla options, however, very little is mentioned on hedging a portfolio of structured products such as Autocallable notes. Hedging structured products is much more complex and the traditional RL approaches tend to fail in this context due to the underlying complexity of these products. These are more complicated due to presence of several barriers and coupon payments, and having a longer maturity date (from $7$ years to a decade), etc. In this direction, we propose a distributional RL based method to hedge a portfolio containing an Autocallable structured note. We will demonstrate our RL hedging strategy using American and Digital options as hedging instruments. Through several empirical analysis, we will show that distributional RL provides better $PnL$ distribution than traditional approaches and learns a better policy depicting lower value-at-risk ($VaR$) and conditional value-at-risk ($CVaR$), showcasing the potential for enhanced risk management.

翻译：量化金融研究表明，强化学习方法在对冲金融投资组合方面展现出良好前景。例如，使用强化学习对冲欧式期权组合能获得比Delta中性、Delta-Gamma中性等传统对冲策略更优的盈亏分布[Cao et. al. 2020]。现有研究多集中于普通期权对冲，但对结构化产品（如自动赎回票据）投资组合的对冲探讨甚少。结构化产品对冲更为复杂，因其包含多重障碍条款、票息支付及较长到期期限（通常为7年至十年），传统强化学习方法在此场景下往往失效。为此，我们提出一种基于分布强化学习的方法，用于对冲包含自动赎回结构化票据的投资组合。我们将以美式期权和二元期权作为对冲工具，演示该强化学习对冲策略。通过多项实证分析，我们将证明分布强化学习能产生优于传统方法的盈亏分布，并习得具有更低风险价值与条件风险价值的策略，展现出提升风险管理能力的潜力。