  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.
