多期波动率预测中的部署侧自适应性
Deployment-Side Adaptiveness in Multi-Horizon Volatility Forecasting
AI总结：
研究多期波动率预测中，通过改变推理时的展开规则来提升预测性能，发现验证集选择的部署策略能有效改善MIMO模型性能，且策略选择对评估指标敏感。
摘要：
在金融预测中，预测性能不仅取决于训练哪个模型，还取决于训练好的模型如何部署。我们在多期波动率预测中研究这一问题。我们的出发点是，训练好的多输出（MIMO）预测器并不定义一个单一的可部署预测器：通过改变推理时的展开规则，同一个训练好的模型会诱导出一族具有不同精度和成本特征的预测。在20个股票波动率序列、三个预测期以及从线性模型到PatchTST的架构中，我们发现非默认的展开规则通常优于标准MIMO部署。然而，最佳固定规则在不同架构和预测期之间差异很大，使得任何单一的静态替换都不可靠。因此，我们在诱导的规则族上评估基于验证的部署策略。在主要MSE目标下，验证选择的单规则提供了比默认MIMO成本更低的改进，而小规则子集以显著更低的推理成本恢复了大部分大集成的好处。我们还发现策略排名对指标敏感：MSE选择的策略不能一致地转移到QLIKE（一种金融标准波动率损失）上。这些结果表明，推理时部署是金融预测中一个有意义的自适应来源，并且训练好的波动率预测器不仅应根据其架构，还应根据其部署策略进行评估。
英文摘要：
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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