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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.01446 |
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| _version_ | 1866916985065963520 |
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| author | Milyushkov, Georgy |
| author_facet | Milyushkov, Georgy |
| contents | This study investigates the application of machine learning techniques, specifically Neural Networks, Random Forests, and CatBoost for option pricing, in comparison to traditional models such as Black-Scholes and Heston Model. Using both synthetically generated data and real market option data, each model is evaluated in predicting the option price. The results show that machine learning models can capture complex, non-linear relationships in option prices and, in several cases, outperform both Black-Scholes and Heston models. These findings highlight the potential of data-driven methods to improve pricing accuracy and better reflect market dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_01446 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Can Machine Learning Algorithms Outperform Traditional Models for Option Pricing? Milyushkov, Georgy Computational Finance This study investigates the application of machine learning techniques, specifically Neural Networks, Random Forests, and CatBoost for option pricing, in comparison to traditional models such as Black-Scholes and Heston Model. Using both synthetically generated data and real market option data, each model is evaluated in predicting the option price. The results show that machine learning models can capture complex, non-linear relationships in option prices and, in several cases, outperform both Black-Scholes and Heston models. These findings highlight the potential of data-driven methods to improve pricing accuracy and better reflect market dynamics. |
| title | Can Machine Learning Algorithms Outperform Traditional Models for Option Pricing? |
| topic | Computational Finance |
| url | https://arxiv.org/abs/2510.01446 |