Option Pricing with Convolutional Kolmogorov-Arnold Networks
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929610514497536 |
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| author | Li, Zeyuan Huang, Qingdao |
| author_facet | Li, Zeyuan Huang, Qingdao |
| contents | With the rapid advancement of neural networks, methods for option pricing have evolved significantly. This study employs the Black-Scholes-Merton (B-S-M) model, incorporating an additional variable to improve the accuracy of predictions compared to the traditional Black-Scholes (B-S) model. Furthermore, Convolutional Kolmogorov-Arnold Networks (Conv-KANs) and Kolmogorov-Arnold Networks (KANs) are introduced to demonstrate that networks with enhanced non-linear capabilities yield superior fitting performance. For comparative analysis, Conv-LSTM and LSTM models, which are widely used in time series forecasting, are also applied. Additionally, a novel data selection strategy is proposed to simulate a real trading environment, thereby enhancing the robustness of the model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_01224 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Option Pricing with Convolutional Kolmogorov-Arnold Networks Li, Zeyuan Huang, Qingdao Computational Engineering, Finance, and Science With the rapid advancement of neural networks, methods for option pricing have evolved significantly. This study employs the Black-Scholes-Merton (B-S-M) model, incorporating an additional variable to improve the accuracy of predictions compared to the traditional Black-Scholes (B-S) model. Furthermore, Convolutional Kolmogorov-Arnold Networks (Conv-KANs) and Kolmogorov-Arnold Networks (KANs) are introduced to demonstrate that networks with enhanced non-linear capabilities yield superior fitting performance. For comparative analysis, Conv-LSTM and LSTM models, which are widely used in time series forecasting, are also applied. Additionally, a novel data selection strategy is proposed to simulate a real trading environment, thereby enhancing the robustness of the model. |
| title | Option Pricing with Convolutional Kolmogorov-Arnold Networks |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2412.01224 |