We study U.S. Treasury yield curve forecasting under distributional uncertainty and recast forecasting as an operations research and managerial decision problem. Rather than minimizing average forecast error, the forecaster selects a decision rule that minimizes worst case expected loss over an ambiguity set of forecast error distributions. To this end, we propose a distributionally robust ensemble forecasting framework that integrates parametric factor models with high dimensional nonparametric machine learning models through adaptive forecast combinations. The framework consists of three machine learning components. First, a rolling window Factor Augmented Dynamic Nelson Siegel model captures level, slope, and curvature dynamics using principal components extracted from economic indicators. Second, Random Forest models capture nonlinear interactions among macro financial drivers and lagged Treasury yields. Third, distributionally robust forecast combination schemes aggregate heterogeneous forecasts under moment uncertainty, penalizing downside tail risk via expected shortfall and stabilizing second moment estimation through ridge regularized covariance matrices. The severity of the worst case criterion is adjustable, allowing the forecaster to regulate the trade off between robustness and statistical efficiency. Using monthly data, we evaluate out of sample forecasts across maturities and horizons from one to twelve months ahead. Adaptive combinations deliver superior performance at short horizons, while Random Forest forecasts dominate at longer horizons. Extensions to global sovereign bond yields confirm the stability and generalizability of the proposed framework.

翻译：本研究在分布不确定性条件下探讨美国国债收益率曲线预测问题，并将预测重构为运筹学与管理决策问题。预测者不再以最小化平均预测误差为目标，而是选择在预测误差分布的模糊集合上最小化最坏情况期望损失的决策规则。为此，我们提出一个分布鲁棒的集成预测框架，通过自适应预测组合将参数化因子模型与高维非参数机器学习模型相融合。该框架包含三个机器学习组件：首先，滚动窗口因子增强动态Nelson-Siegel模型利用从经济指标中提取的主成分捕捉水平、斜率和曲率动态；其次，随机森林模型捕捉宏观金融驱动因素与滞后国债收益率之间的非线性交互作用；第三，分布鲁棒的预测组合方案在矩不确定性条件下聚合异质预测，通过期望短缺惩罚下行尾部风险，并借助岭正则化协方差矩阵稳定二阶矩估计。最坏情况准则的严重程度可调，使预测者能够权衡鲁棒性与统计效率。基于月度数据的实证研究表明：在1至12个月期限范围内，自适应组合在短期预测中表现优异，而随机森林在长期预测中占据主导。对全球主权债券收益率的扩展分析验证了该框架的稳定性与泛化能力。