Applying Informer for Option Pricing: A Transformer-Based Approach

TL;DR

This paper explores using the Informer neural network, a transformer-based model, to improve option pricing accuracy and adaptability in volatile financial markets, surpassing traditional models like Black-Scholes.

Contribution

It introduces the application of the Informer architecture to option pricing, demonstrating its superior performance and flexibility over existing methods.

Findings

Informer outperforms traditional models in accuracy

The model adapts better to market volatility

Enhanced prediction stability in financial forecasting

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.