Dynamic Replication and Hedging: A Reinforcement Learning Approach
By Petter N. Kolm, Gordon Ritter. 2019
87 citations recorded by the source corpus.
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.
Subfields: Finance, Management Science and Operations Research
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Citations in the corpus
- Enhancing Time-Series Momentum Strategies Using Deep Neural Networks (2019, background): “RL is particularly of interest within the context of optimal execution and automated hedging [42, 43] for example, where actions taken can have an impact on future states of the world (e.”
- Recent advances in reinforcement learning in finance (2021, methodology): “Popular choices of performance measure include Implementation Shortfall (Hendricks . . . Wilcox, 2014; Hambly et al., 2021), PnL (with a penalty term of transaction cost) (Deng et al., 2017; Ning et al., 2018; Wei et al., 2019), trading cost (Nevmyvaka et al., 2006; Shen et al., 2014), profit (Deng ”