用Transformer模型Informer提升期权定价准确率。
Applying Informer for Option Pricing: A Transformer-Based Approach
- 采用Informer模型捕捉市场长期依赖关系。
- 在波动市场中表现优于传统定价模型。
- 适合追求高精度金融预测的研究者与从业者。
准确的期权定价对金融市场交易与风险管理至关重要，但受市场波动及传统模型（如Black-Scholes）局限，仍具挑战性。本文研究Informer神经网络在期权定价中的应用，利用其捕捉长期依赖和动态适应市场变化的能力。该研究通过引入Informer的高效架构，提升了预测准确性，构建了比现有方法更灵活、更具韧性的金融预测框架。实验结果表明，Informer在期权定价任务中优于传统方法，显著增强了数据驱动型金融预测的能力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。