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- W2914584772 abstract "The authors of this article address the problem of how to optimally hedge an options book in a practical setting, where trading decisions are discrete and trading costs can be nonlinear and difficult to model. Based on reinforcement learning (RL), a well-established machine learning technique, the authors propose a model that is flexible, accurate and very promising for real-world applications. A key strength of the RL approach is that it does not make any assumptions about the form of trading cost. RL learns the minimum variance hedge subject to whatever transaction cost function one provides. All that it needs is a good simulator, in which transaction costs and options prices are simulated accurately." @default.
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- W2914584772 date "2019-01-31" @default.
- W2914584772 modified "2023-10-13" @default.
- W2914584772 title "Dynamic Replication and Hedging: <i>A Reinforcement Learning Approach</i>" @default.
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- W2914584772 doi "https://doi.org/10.3905/jfds.2019.1.1.159" @default.
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