Forecasting the U.S. Treasury Yield Curve: A Distributionally Robust Machine Learning Approach for Interest Rate Risk Management

Abstract

U.S. Treasury yields are central to global asset pricing but are noisy and subject to policy uncertainty, supply-demand forces, and behavioral effects, exposing forecast users to downside risk. We formulate yield curve forecasting as a decision problem under distributional uncertainty and propose a distributionally robust ensemble framework that combines parametric factor models with machine-learning forecasts. A factor-augmented Dynamic Nelson-Siegel model captures yield-curve dynamics, while Random Forests model nonlinear interactions. Robust forecast combinations penalize tail risk and improve out-of-sample performance across maturities. The framework supports disciplined $DV01$-based interest-rate risk management for corporate, institutional and balance-sheet decision makers.