Exact Terminal Condition Neural Network for American Option Pricing Based on the Black-Scholes-Merton Equations
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912679203962880 |
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| author | Zhang, Wenxuan Guo, Yixiao Lu, Benzhuo |
| author_facet | Zhang, Wenxuan Guo, Yixiao Lu, Benzhuo |
| contents | This paper proposes the Exact Terminal Condition Neural Network (ETCNN), a deep learning framework for accurately pricing American options by solving the Black-Scholes-Merton (BSM) equations. The ETCNN incorporates carefully designed functions that ensure the numerical solution not only exactly satisfies the terminal condition of the BSM equations but also matches the non-smooth and singular behavior of the option price near expiration. This method effectively addresses the challenges posed by the inequality constraints in the BSM equations and can be easily extended to high-dimensional scenarios. Additionally, input normalization is employed to maintain the homogeneity. Multiple experiments are conducted to demonstrate that the proposed method achieves high accuracy and exhibits robustness across various situations, outperforming both traditional numerical methods and other machine learning approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_27132 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exact Terminal Condition Neural Network for American Option Pricing Based on the Black-Scholes-Merton Equations Zhang, Wenxuan Guo, Yixiao Lu, Benzhuo Computational Finance This paper proposes the Exact Terminal Condition Neural Network (ETCNN), a deep learning framework for accurately pricing American options by solving the Black-Scholes-Merton (BSM) equations. The ETCNN incorporates carefully designed functions that ensure the numerical solution not only exactly satisfies the terminal condition of the BSM equations but also matches the non-smooth and singular behavior of the option price near expiration. This method effectively addresses the challenges posed by the inequality constraints in the BSM equations and can be easily extended to high-dimensional scenarios. Additionally, input normalization is employed to maintain the homogeneity. Multiple experiments are conducted to demonstrate that the proposed method achieves high accuracy and exhibits robustness across various situations, outperforming both traditional numerical methods and other machine learning approaches. |
| title | Exact Terminal Condition Neural Network for American Option Pricing Based on the Black-Scholes-Merton Equations |
| topic | Computational Finance |
| url | https://arxiv.org/abs/2510.27132 |