Applying Informer for Option Pricing: A Transformer-Based Approach
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908395891589120 |
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| author | Bańka, Feliks Chudziak, Jarosław A. |
| author_facet | Bańka, Feliks Chudziak, Jarosław A. |
| contents | Accurate option pricing is essential for effective trading and risk management in financial markets, yet it remains challenging due to market volatility and the limitations of traditional models like Black-Scholes. In this paper, we investigate the application of the Informer neural network for option pricing, leveraging its ability to capture long-term dependencies and dynamically adjust to market fluctuations. This research contributes to the field of financial forecasting by introducing Informer's efficient architecture to enhance prediction accuracy and provide a more adaptable and resilient framework compared to existing methods. Our results demonstrate that Informer outperforms traditional approaches in option pricing, advancing the capabilities of data-driven financial forecasting in this domain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05565 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Applying Informer for Option Pricing: A Transformer-Based Approach Bańka, Feliks Chudziak, Jarosław A. Computational Engineering, Finance, and Science Artificial Intelligence Machine Learning Computational Finance 91G60, 68T07 I.2.6; J.4 Accurate option pricing is essential for effective trading and risk management in financial markets, yet it remains challenging due to market volatility and the limitations of traditional models like Black-Scholes. In this paper, we investigate the application of the Informer neural network for option pricing, leveraging its ability to capture long-term dependencies and dynamically adjust to market fluctuations. This research contributes to the field of financial forecasting by introducing Informer's efficient architecture to enhance prediction accuracy and provide a more adaptable and resilient framework compared to existing methods. Our results demonstrate that Informer outperforms traditional approaches in option pricing, advancing the capabilities of data-driven financial forecasting in this domain. |
| title | Applying Informer for Option Pricing: A Transformer-Based Approach |
| topic | Computational Engineering, Finance, and Science Artificial Intelligence Machine Learning Computational Finance 91G60, 68T07 I.2.6; J.4 |
| url | https://arxiv.org/abs/2506.05565 |