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Hedging Barrier Options Using Reinforcement Learning

35.00 mins
Professor John Hull
Thu 21 Sep 2023

We examine the advantages of using reinforcement learning (RL) to hedge barrier options and other similar exotic options. We find that, when the hedger’s objective is to minimize VaR95 or CVaR95, RL is an attractive alternative to other hedging approaches. RL requires an assumption about the stochastic process followed by the underlying asset during the life of the exotic option, but our tests show that the results from using RL are fairly robust to this assumption. We do not consider transaction costs in this research. However, we show that RL involves less trading than other hedging approaches. As a result, the existence of transaction costs can be expected to increase the attractiveness of RL.