Forecasting Realized Volatility with Time Series Foundation Models: A Comparison with Econometric Benchmarks
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时间序列基础模型预测已实现波动率：与计量基准的比较
- 研究问题
- 预训练时间序列基础模型能否改善已实现波动率预测，其优势来自动态信息、预测尺度，还是少数资产的影响？
- 方法与思路
- 作者在五十项资产与三个期限上比较九个零样本基础模型和八个计量规格，并采用预测比较检验、重新校准及模型组合。
- 材料陈述的贡献
- 摘要称基础模型没有普遍优势，TTM 相对 Log-HAR 的优势较小；二者等权组合表现稳健，模型架构选择影响突出。
- 阅读关注点
- 应核对损失汇总与逐资产标准化的差别、重新校准的实施方式，以及月度信息增益和组合表现的统计证据。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；结论限于摘要所述数据与比较设置，不能推广为普遍优势。
原文摘要
We ask whether pretrained time series foundation models (TSFMs) improve on established econometric benchmarks for forecasting realized volatility. Using the VOLARE dataset, we conduct the first systematic comparison of nine zero-shot TSFMs against eight econometric specifications, including the Heterogeneous Autoregressive (HAR) family, across 50 assets in equities, foreign exchange, and futures, and three forecast horizons, with formal pairwise and multi-model forecast-comparison tests. Foundation models do not deliver a uniform gain. Pooled losses favor them, but the advantage is concentrated in a few outlier assets; averaging each asset's loss ratio to a well-specified Log-HAR benchmark, so that no single asset dominates, only one small model, Tiny Time Mixers (TTM), beats the benchmark at every horizon, and by a narrow margin. The other foundation models do not improve on Log-HAR, and the econometric benchmarks remain competitive throughout. A Mincer--Zarnowitz recalibration, which removes level and scale bias from every forecast, shows that much of the short-horizon advantage reflects better-scaled forecasts rather than better prediction of volatility dynamics, and only at the monthly horizon does a genuine informational gain remain. Because this edge is thin and even TTM is not best on every asset, a simple equal-weight average of TTM and Log-HAR matches the best single model and enters the Model Confidence Set for 98 to 100\% of assets, more often than either component alone, so a forecaster need not identify the best model for each asset in advance. Our most durable finding is that performance varies so much across foundation-model architectures that choosing the right architecture matters more than the broader choice between foundation and econometric models.