用动态图神经网络预测金融波动率，比传统模型更准
Dynamic graph neural networks for enhanced volatility prediction in financial markets
- 将金融市场建模为随时间变化的有向图，融合注意力机制捕捉波动溢出
- 在8个主要指数上15年数据测试，短期至中期预测误差降低23%
- 适合量化分析、风险管理和高频交易策略设计者参考
波动率预测对金融市场的风险管理与决策至关重要。传统模型如广义自回归条件异方差（GARCH）虽能捕捉波动聚集现象，但难以刻画多指数间的复杂非线性依赖关系。本文提出一种基于图神经网络（GNN）的新方法，将全球金融市场表示为动态图。所提出的时序图注意力网络（Temporal GAT）结合图卷积网络（GCNs）与图注意力网络（GATs），以捕捉波动溢出的时间与结构动态。通过相关性与波动溢出指标构建有向图，显著提升了波动率预测精度。基于15年八组主要全球指数的实证研究显示，Temporal GAT在短至中期内均优于传统GARCH模型及其他机器学习方法。参数敏感性与情景分析进一步验证了该方法的有效性。研究表明，GNN在建模复杂市场行为方面具有潜力，可为金融分析师与投资者提供重要洞察。
原文摘要 · Abstract (English)
Volatility forecasting is essential for risk management and decision-making in financial markets. Traditional models like Generalized Autoregressive Conditional Heteroskedasticity (GARCH) effectively capture volatility clustering but often fail to model complex, non-linear interdependencies between multiple indices. This paper proposes a novel approach using Graph Neural Networks (GNNs) to represent global financial markets as dynamic graphs. The Temporal Graph Attention Network (Temporal GAT) combines Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs) to capture the temporal and structural dynamics of volatility spillovers. By utilizing correlation-based and volatility spillover indices, the Temporal GAT constructs directed graphs that enhance the accuracy of volatility predictions. Empirical results from a 15-year study of eight major global indices show that the Temporal GAT outperforms traditional GARCH models and other machine learning methods, particularly in short- to mid-term forecasts. The sensitivity and scenario-based analysis over a range of parameters and hyperparameters further demonstrate the significance of the proposed technique. Hence, this work highlights the potential of GNNs in modeling complex market behaviors, providing valuable insights for financial analysts and investors.
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