AI 中文总结
本文对比两种机器学习预测方法，发现宏观经济预测因子可提升收益率曲线斜率预测效果，加入宏观变量的模型在交易模拟中表现最优，同时指出统计与经济性能存在差距。
AI 中文摘要
本文采用通用高维宏观经济信息集，比较直接收益率法与因子法对美国国债收益率曲线的预测效果。在2015-2025年的样本外期间，对月度零息收益率进行预测评估。随机游走模型的预测增益集中在短期期限和斜率预测上，并随预测期限延长而下降。直接收益率模型在斜率预测中表现最佳，在短期期限中相对更强，而因子法在较长期限中更具竞争力。宏观经济预测因子提供了明显的增量预测能力，对与斜率相关的变动作用最强。交易模拟进一步证实，加入宏观变量的模型在斜率交易中表现最佳。该模拟还凸显了统计性能与经济性能之间的差距，因为随机游走模型在统计损失下是强劲基准，但作为交易信号表现不佳。
英文摘要
This paper compares direct-yield and factor-based approaches to U.S. Treasury yield curve forecasting using a common high-dimensional macroeconomic information set. Forecasts are evaluated on monthly zero-coupon yields over the 2015-2025 out-of-sample period. Gains over the random walk are concentrated at short maturities and in slope forecasts, and decline with the forecast horizon. Direct-yield models perform best for slope forecasts and are relatively stronger at short horizons, while factor-based models become more competitive at longer horizons. Macroeconomic predictors provide clear incremental predictive power, strongest for slope-related movements. A trading simulation reinforces that macro-augmented models perform best in slope trades. The simulation also highlights a gap between statistical and economic performance, as the random walk is a strong benchmark under statistical loss but performs poorly as a trading signal.
