Applying Informer for Option Pricing: A Transformer-Based Approach
This work addresses the challenge of option pricing for traders and risk managers, but it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of accurate option pricing in financial markets by applying the Informer neural network, a transformer-based approach, and demonstrated that it outperforms traditional methods like Black-Scholes.
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.