Insights on Time-consistent Deep Hedging under Elicitable Dynamic Risk Measures

We study deep hedging where the objective is expressed in terms of a dynamic risk measure, under which sequential decisions are time-consistent. We tackle the problem of basket option hedging. We show that the approach is feasible and can be used conveniently in the presence of more complex state spaces with higher dimension than what was previously test in the literature. We rely on the conditional elicitability of spectral risk measures to represent the optimization objective. We provide insights on how the choice of scoring function impacts the training of the hedging agent. We also propose an adapted reinforcement learning (RL) algorithm to solve the problem, which contains additions that are crucial to a successful training of the deep RL agent, and without which the agent fails to converge to proper solutions. The time-consistent hedging strategies are also benchmarked against deep hedging approaches relying on static risk measures leading to precommitment.