用视觉Transformer预测30天真实波动率，挖掘隐含波动率表面非线性特征。
Data-Efficient Realized Volatility Forecasting with Vision Transformers
- 将图像模型ViT用于期权隐含波动率曲面，输入单日数据预测未来30天波动率。
- 模型能捕捉波动率曲面中的季节性模式与非线性关系，表现优于传统方法。
- 适合关注金融时序建模、多变量非线性关系的量化研究者。
近期金融机器学习研究显示，复杂模型因其学习高度非线性关系的能力，在金融预测中优于简单方法。尽管Informer等变压器架构在金融时间序列预测中表现良好，但其在期权数据上的应用仍基本空白。本文初步探索了针对期权数据的变压器模型开发，采用通常用于现代图像识别与分类的视觉变压器（Vision Transformer, ViT）架构，从单日的隐含波动率曲面（附加日期信息）中预测未来30天的资产真实波动率。结果表明，ViT能够从隐含波动率曲面中学习季节性模式与非线性特征，为后续模型发展提供了有前景的方向。
原文摘要 · Abstract (English)
Recent work in financial machine learning has shown the virtue of complexity: the phenomenon by which deep learning methods capable of learning highly nonlinear relationships outperform simpler approaches in financial forecasting. While transformer architectures like Informer have shown promise for financial time series forecasting, the application of transformer models for options data remains largely unexplored. We conduct preliminary studies towards the development of a transformer model for options data by training the Vision Transformer (ViT) architecture, typically used in modern image recognition and classification systems, to predict the realized volatility of an asset over the next 30 days from its implied volatility surface (augmented with date information) for a single day. We show that the ViT can learn seasonal patterns and nonlinear features from the IV surface, suggesting a promising direction for model development.
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