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

• URL: http://arxiv.org/abs/2608.12251v1
• Date: Wed, 12 Aug 2026 16:51:51 GMT
• Title: Regime-Gated Residual Mixture-of-Experts for Cross-Sectional Volatility Forecasting
• Abstract summary: We study five-day realized-volatility forecasts for 1,027 U.S. equities using a walk-forward evaluation framework.<n>We propose RG-ResMoE, a regime-gated residual mixture-of-experts architecture.
• Score: 2.4235475271758076
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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