发表机构
Tongji University; Stanford University; Tsinghua University; MIT（同济大学; 斯坦福大学; 清华大学; 麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出FA-GSTN模型，将RV预测重构为结构化金融对象演化建模，在股票期权数据集上实现最优预测精度，且数据效率与市场压力下的鲁棒性均优于基准模型。
AI 中文摘要
准确预测已实现波动率（$RV$）对风险管理和衍生品定价至关重要。尽管隐含波动率（$IV$）曲面蕴含丰富信息，但主流方法将其视为静态图像，无法捕捉其内在动态。为克服这一局限，我们提出金融感知图时空网络（Finance-Aware Graph Spatio-Temporal Network, FA-GSTN），一种将$RV$预测重构为结构化金融对象演化建模的新型架构。FA-GSTN从$IV$曲面构建时空图序列，其中节点对应网格点，边编码自适应空间（日内）和显式时间（日间）依赖关系。该模型通过金融感知节点特征（如期权希腊字母）融入领域知识，结合多尺度时间平滑门与自适应鲁棒损失函数处理高频噪声。在大规模股票期权数据集上的综合评估显示，FA-GSTN达到新的最优性能，预测精度（$R^2$）最高达0.473；在仅使用一年数据训练时，其数据效率显著优于强基准模型Vision Transformer（$R^2$：0.372 vs. 0.315）；在2020-2021年等市场压力时期，该模型表现出更强的鲁棒性。消融研究证实时空图结构、金融感知组件及集成噪声处理模块均发挥关键作用。本研究凸显显式建模时间动态与融入金融归纳偏置对实现准确、鲁棒的波动率预测具有重大价值。
英文摘要
Accurate forecasting of realized volatility ($RV$) is crucial for risk management and derivatives pricing. Although the implied volatility ($IV$) surface offers rich informational content, prevailing methods that treat it as a static image fail to capture its inherent dynamics. To overcome this limitation, we propose the Finance-Aware Graph Spatio-Temporal Network (FA-GSTN), a novel architecture that reframes $RV$ forecasting as modeling the evolution of a structured financial object. FA-GSTN builds a spatio-temporal graph sequence from the $IV$ surface, where nodes correspond to grid points and edges encode adaptive spatial (intra-day) and explicit temporal (inter-day) dependencies. The model incorporates domain knowledge through finance-aware node features (e.g., option Greeks) and tackles high-frequency noise via a multi-scale temporal smoothing gate coupled with an adaptive robust loss function. Comprehensive evaluations on a large-scale equity options dataset show that FA-GSTN sets a new state of the art, delivering superior predictive accuracy ($R^2$ up to 0.473). It also demonstrates remarkable data efficiency, substantially outperforming strong Vision Transformer baselines when trained on only one year of data ($R^2$: 0.372 vs. 0.315). Furthermore, the model exhibits enhanced robustness during periods of market stress, such as 2020--2021. Ablation studies confirm the vital roles of the spatio-temporal graph structure, finance-aware components, and integrated noise-handling modules. Our work underscores the substantial benefits of explicitly modeling temporal dynamics and infusing financial inductive biases for accurate and robust volatility forecasting.
Comments15 pages
