Applying Informer for Option Pricing: A Transformer-Based Approach

Feliks Ba\'nka and Jaros{\l}aw A. Chudziak

Papers from arXiv.org

Abstract: 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.

Date: 2025-06
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Published in Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3 (ICAART 2025), pages 1270-1277. SciTePress, 2025

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