波动率聚集自适应用于金融时间序列

中文摘要 AI 辅助

时间序列基础模型越来越多地通过在目标数据上进行微调来适应新领域，其隐含假设是更多的目标数据能带来更好的预测。我们表明，这一假设在金融预测中可能失效，因为金融预测中单个价格变动难以预测，但大幅波动往往聚集，形成交替的平静期和动荡期。利用在开盘价、最高价、最低价、收盘价和成交量的价格柱上训练的金融基础模型，我们认为适应金融领域需要超越下一标记预测的训练信号。我们引入了波动率聚集自适应（VCA），该方法在下一标记交叉熵的基础上增加了一个关于平方收益自相关的可微惩罚项，平方收益自相关是波动率聚集的标准统计特征。这一额外的目标通过将自回归展开的依赖结构与实际未来的依赖结构相匹配，提供了多步训练信号。在三个资产集和两种评估约定下，VCA 相比预训练模型提升了自适应效果，在主要评估（\textsc{fore}）下增益最强，主要得益于方差误差的降低。总体而言，我们的结果表明，有效的金融自适应需要能够捕捉超越标记级预测的领域特定时间结构的目标函数。

英文摘要

Time-series foundation models are increasingly adapted to new domains through fine-tuning on target data, under the implicit assumption that more target data yields better forecasts. We show that this assumption can fail in financial forecasting, where individual price changes are difficult to predict, but large moves tend to cluster, creating alternating calm and turbulent periods. Using financial foundation models trained on price bars of open, high, low, close, and volume, we argue that adapting to financial domains requires training signals beyond next-token prediction. We introduce Volatility-Clustering Adaptation (VCA), which augments next-token cross-entropy with a differentiable penalty on the autocorrelation of squared returns, the standard statistical signature of volatility clustering. This additional objective provides a multi-step training signal by matching the resulting dependence structure of autoregressive rollouts to those of the realized future. Across three asset sets and two evaluation conventions, VCA improves adaptation over the pre-trained model, with the strongest gains under the primary evaluation (\textsc{fore}), driven primarily by reduced variance error. Overall, our results suggest that effective financial adaptation requires objectives that capture domain-specific temporal structure beyond token-level prediction.

发表机构

• Applied Artificial Intelligence Initiative, Deakin University（迪肯大学应用人工智能计划）

机构由 AI 辅助整理，请以论文原文为准。