Applying Informer for Option Pricing: A Transformer-Based Approach

• URL: http://arxiv.org/abs/2506.05565v1
• Date: Thu, 05 Jun 2025 20:23:28 GMT
• Title: Applying Informer for Option Pricing: A Transformer-Based Approach
• Authors: Feliks Bańka, Jarosław A. Chudziak,
• Abstract summary: In this paper, we investigate the application of the Informer neural network for option pricing.<n>This research contributes to the field of financial forecasting by introducing Informer's efficient architecture to enhance prediction accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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