Option Market Making via Reinforcement Learning
Fuente:
arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866916648669151232 |
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| author | Fang, Zhou Xu, Haiqing |
| author_facet | Fang, Zhou Xu, Haiqing |
| contents | Market making of options with different maturities and strikes is a challenging problem due to its highly dimensional nature. In this paper, we propose a novel approach that combines a stochastic policy and reinforcement learning-inspired techniques to determine the optimal policy for posting bid-ask spreads for an options market maker who trades options with different maturities and strikes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_01814 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Option Market Making via Reinforcement Learning Fang, Zhou Xu, Haiqing Trading and Market Microstructure Market making of options with different maturities and strikes is a challenging problem due to its highly dimensional nature. In this paper, we propose a novel approach that combines a stochastic policy and reinforcement learning-inspired techniques to determine the optimal policy for posting bid-ask spreads for an options market maker who trades options with different maturities and strikes. |
| title | Option Market Making via Reinforcement Learning |
| topic | Trading and Market Microstructure |
| url | https://arxiv.org/abs/2307.01814 |