Deployment-Side Adaptiveness in Multi-Horizon Volatility Forecasting

2026-06-25

AI Summary

This work addresses the critical yet underexplored impact of deployment strategies on performance in multi-horizon volatility forecasting. The authors propose an adaptive deployment framework that dynamically selects rollout rules based on validation-set performance, automatically balancing prediction accuracy against inference cost across diverse model architectures—from linear models to PatchTST—and loss functions such as MSE and QLIKE. Experiments on volatility sequences of 20 stocks demonstrate that non-default rollout rules significantly outperform standard multi-output (MIMO) deployment. Notably, a small subset of rollout rules can closely approximate the performance of large ensembles at substantially lower computational overhead, with strategy effectiveness highly dependent on the choice of evaluation metric. This study underscores deployment strategy as a key source of adaptability in financial forecasting, challenging conventional fixed-deployment paradigms.

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.

Problem

• multi-horizon forecasting
• volatility prediction
• deployment policy
• rollout rule
• model adaptiveness

Innovation

• deployment-side adaptiveness
• multi-horizon forecasting
• rollout rule
• volatility prediction
• validation-based policy