制度门控残差混合专家模型预测横截面波动率

在容量匹配实验中，RG-ResMoE在预测准确性和训练稳定性上优于标准MLP，并在日本股票面板上复现了类似增益。将制度变量直接拼接到输入会降低性能，而仅作为路由门控输入则改善预测精度和VaR校准。

软路由一致优于硬路由。

Regime-Gated Residual Mixture-of-Experts for Cross-Sectional Volatility Forecasting

Financial volatility is regime dependent, yet incorporating regime information into neural networks can also destabilize training. This paper asks where such information should enter a neural cross-sectional volatility forecasting model. We study five-day realized-volatility forecasts for 1,027 U.S. equities using a rolling walk-forward evaluation framework in which information, model capacity, hyperparameter tuning, and random seeds are matched across architectures. We propose RG-ResMoE, a regime-gated residual mixture-of-experts architecture in which regime information is used only for expert routing rather than for direct forecasting. The base predictor models volatility from stock features, while a gating network uses regime state variables to route residual corrections. RG-ResMoE consistently outperforms a capacity-matched MLP in both forecasting accuracy and training stability in the main U.S. study. Similar gains are observed on an independent Japanese panel. The integration pathway is decisive: appending the same regime variables directly to the forecasting input degrades both predictive performance and training stability, whereas restricting them to the routing gate improves accuracy and Value-at-Risk calibration. Hard routing consistently underperforms soft routing. The results suggest that, in compact neural volatility forecasting models, the primary value of mixture-of-experts models lies less in increasing model capacity than in controlling how nonstationary regime information influences prediction.

This report is categorized under AITOP's "Papers" desk. Key entities and technical topics under continuous tracking include RG-ResMoE, 混合专家, 波动率预测, 量化金融.

Source attribution: curated from arXiv cs.LG (@Junyi Ye, Gargi Vijay Borde).