🤖 AI Summary
Option pricing faces challenges in modeling market nonlinearity, time-varying volatility, and long-range dependencies. Conventional approaches—such as the Black–Scholes model and LSTM-based methods—exhibit limited robustness and adaptability to rapidly evolving financial dynamics. This paper introduces, for the first time, the lightweight and efficient Informer architecture to option pricing. Leveraging ProbSparse self-attention, distilling encoders, and a generative decoder, Informer effectively captures the dynamic structure of high-frequency, heterogeneous financial time series. The proposed data-driven framework significantly enhances real-time responsiveness to market regime shifts and improves generalization capability. Empirical evaluations across multiple markets demonstrate that the method reduces average pricing error by 37% relative to both Black–Scholes and LSTM baselines. Moreover, it achieves superior prediction stability and markedly improved cross-maturity and cross-contract generalization. This work establishes a novel paradigm for high-accuracy, low-latency derivative pricing.
📝 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.