RLOP: RL Methods in Option Pricing from a Mathematical Perspective
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Abstract (arXiv)
Abstract In this work, we build two environments, namely the modified QLBS and RLOP models, from a mathematics perspective which enables RL methods in option pricing through replicating by portfolio. We implement the environment specifications (the source code can be found at https://github.com/owen8877/RLOP), the learning algorithm, and agent parametrization by a neural network. The learned optimal hedging strategy is compared against the BS prediction. The effect of various factors is considered and studied based on how they affect the optimal price and position.
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References · 5
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2015Policy Gradient for Coherent Risk Measurescited by 74
- 2017QLBS: Q-Learner in the Black-Scholes(-Merton) Worldscited by 23
- 2021Distributional Offline Continuous-Time Reinforcement Learning with Neural Physics-Informed PDEs (SciPhy RL for DOCTR-L)cited by 6
- 2021Deep Reinforcement Learning for Equal Risk Pricing and Hedging under Dynamic Expectile Risk Measurescited by 3
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