Applying Informer for Option Pricing: A Transformer-Based Approach

Feliks Bańka, Jarosław A. Chudziak

2025

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.

Paper Citation

in Harvard Style

Bańka F. and Chudziak J. (2025). Applying Informer for Option Pricing: A Transformer-Based Approach. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 1270-1277. DOI: 10.5220/0013320900003890

in Bibtex Style

@conference{icaart25,
author={Feliks Bańka and Jarosław Chudziak},
title={Applying Informer for Option Pricing: A Transformer-Based Approach},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={1270-1277},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013320900003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Applying Informer for Option Pricing: A Transformer-Based Approach
SN - 978-989-758-737-5
AU - Bańka F.
AU - Chudziak J.
PY - 2025
SP - 1270
EP - 1277
DO - 10.5220/0013320900003890
PB - SciTePress