Deployment-Side Adaptiveness in Multi-Horizon Volatility Forecasting

• URL: http://arxiv.org/abs/2606.27688v1
• Date: Fri, 26 Jun 2026 03:32:09 GMT
• Title: Deployment-Side Adaptiveness in Multi-Horizon Volatility Forecasting
• Abstract summary: We study how a trained multi-output (MIMO) forecaster induces a family of forecasts with different accuracy and cost profiles.<n>Across 20 stock-volatility series, three forecast horizons, and architectures ranging from linear models to PatchTST, we find that non-default rollout rules often improve.<n>We evaluate validation-based deployment policies over the induced rule family.
• Score: 1.2489632787815885
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In financial forecasting, predictive performance depends not only on which model is trained, but also on how the trained model is deployed. We study this issue in multi-horizon volatility forecasting. Our starting point is that a trained multi-output (MIMO) forecaster does not define a single deployable predictor: by changing the inference-time rollout rule, the same trained model induces a family of forecasts with different accuracy and cost profiles. Across 20 stock-volatility series, three forecast horizons, and architectures ranging from linear models to PatchTST, we find that non-default rollout rules often improve over standard MIMO deployment. However, the best fixed rule varies substantially across architectures and horizons, making any single static replacement unreliable. We therefore evaluate validation-based deployment policies over the induced rule family. Under the primary MSE objective, validation-selected singletons provide a low-cost improvement over default MIMO, while small rule subsets recover much of the benefit of larger ensembles at substantially lower inference cost. We also find that policy rankings are metric-sensitive: MSE-selected policies do not transfer uniformly to QLIKE, a finance-standard volatility loss. These results show that inference-time deployment is a meaningful source of adaptiveness in financial forecasting, and that trained volatility forecasters should be evaluated not only by their architecture, but also by their deployment policy.

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