Stripping the Discount Curve—A Robust Machine Learning Approach
提出一种稳健、灵活且易实现的非参数方法，从国债数据中估计收益率曲线，在实证中显著优于现有基准，对异常值和数据选择具有鲁棒性。
We introduce a robust, flexible, and easy-to-implement method for estimating the yield curve from treasury securities. Our nonparametric method learns the discount curve in a function space that we motivate by economic principles. We show in an extensive empirical study on U.S. Treasury securities that our method strongly dominates all parametric and nonparametric benchmarks. It achieves substantially smaller out-of-sample yield and pricing errors while being robust to outliers and data selection choices. We attribute the superior performance to the optimal trade-off between flexibility and smoothness, which positions our method as the new standard for yield curve estimation. This paper was accepted by Agostino Capponi, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.01401 .