May 14, 2024, 4:43 a.m. | Reilly Pickard, Finn Wredenhagen, Julio DeJesus, Mario Schlener, Yuri Lawryshyn

cs.LG updates on

arXiv:2405.06774v1 Announce Type: cross
Abstract: This article leverages deep reinforcement learning (DRL) to hedge American put options, utilizing the deep deterministic policy gradient (DDPG) method. The agents are first trained and tested with Geometric Brownian Motion (GBM) asset paths and demonstrate superior performance over traditional strategies like the Black-Scholes (BS) Delta, particularly in the presence of transaction costs. To assess the real-world applicability of DRL hedging, a second round of experiments uses a market calibrated stochastic volatility model to train …

abstract agents article arxiv cs.lg ddpg delta gradient performance policy q-fin.rm reinforcement reinforcement learning strategies type

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