图神经网络预测波动率，但精准预测不等于更好投资组合。
Do Better Volatility Forecasts Lead to Better Portfolios? Evidence from Graph Neural Networks
- 用滚动相关、行业和因果图构建图神经网络预测波动率。
- 不同模型在预测误差、排序准确性和投资组合收益上表现各异。
- 只有当投资策略能利用图结构时，波动率模型才真正有用。
本文检验图神经网络能否提升实际波动率预测精度，并进一步改善投资组合表现。基于2015-2025年465只标普500成分股的周度实际波动率数据，将异质自回归与长短期记忆基准模型，与基于滚动相关图、行业图及格兰杰因果图构建的GraphSAGE模型进行对比，包含与不包含宏观状态特征。实证发现：最小预测均方误差的模型、最高截面排序准确率的模型、最高投资组合夏普比率的模型，是三个不同的模型。预测精度、排序质量与组合绩效虽有关联，但不可互换。图波动率模型仅在投资规则能利用其编码的截面结构时才具有实际价值。
原文摘要 · Abstract (English)
This paper tests whether graph neural networks improve realized volatility forecasts and whether those forecasts improve portfolio performance. Using weekly realized volatility for 465 S&P 500 equities from 2015-2025, Heterogeneous Autoregressive and Long Short-Term Memory baselines are compared against GraphSAGE models built on rolling correlation, sector, and Granger-causal graphs, with and without macro regime features. The empirical finding is that the model with the lowest forecast MSE, the model with the highest cross-sectional ranking accuracy, and the model with the highest portfolio Sharpe ratio are three different models. Forecast accuracy, ranking quality, and portfolio performance are related but not interchangeable objectives. Graph volatility models add value only when the portfolio rule can exploit the cross-sectional structure they encode.
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