Applying Informer for Option Pricing: A Transformer-Based Approach

ArXiv ID: 2506.05565 “View on arXiv”

Authors: Feliks Bańka, Jarosław A. Chudziak

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.

Keywords: option pricing, Informer neural network, long-term dependency modeling, volatility modeling, time-series forecasting, Derivatives (Options)

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 5.5/10
  • Quadrant: Holy Grail
  • Why: The paper involves advanced deep learning architecture (Informer with ProbSparse attention, O(L log L) complexity) and necessary financial data preprocessing/normalization, indicating moderate-to-high mathematical density. Empirical rigor is moderate due to explicit benchmarking against traditional models and profit analysis on historical data, though it lacks code or dataset specifics, suggesting it’s backtest-ready but not fully implemented.
  flowchart TD
    A["Research Goal:<br>Option Pricing with Informer"] --> B["Data & Input<br>Options Data & Market Volatility"]
    B --> C["Methodology<br>Informer Transformer Architecture"]
    C --> D["Process<br>Long-term Dependency Modeling"]
    D --> E["Outcome<br>Outperforms Black-Scholes & Traditional Models"]
    E --> F["Result<br>Enhanced Prediction Accuracy & Adaptability"]