Publication

Stripping the Discount Curve—A Robust Machine Learning Approach

2025

Abstract

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 .

Official source

https://infoscience.epfl.ch/entities/publication/a81ec513-2574-4da7-9dcf-b6ed081ca8c8

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2025

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/a81ec513-2574-4da7-9dcf-b6ed081ca8c8

About this result

Related concepts (26)

Model selection

Model selection is the task of selecting a model from among various candidates on the basis of performance criterion to choose the best one. In the context of learning, this may be the selection of a statistical model from a set of candidate models, given data. In the simplest cases, a pre-existing set of data is considered. However, the task can also involve the design of experiments such that the data collected is well-suited to the problem of model selection.

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Feature selection is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Stylometry and DNA microarray analysis are two cases where feature selection is used. It should be distinguished from feature extraction. Feature selection techniques are used for several reasons: simplification of models to make them easier to interpret by researchers/users, shorter training times, to avoid the curse of dimensionality, improve data's compatibility with a learning model class, encode inherent symmetries present in the input space.

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In voting systems, the Minimax Condorcet method (often referred to as "the Minimax method") is one of several Condorcet methods used for tabulating votes and determining a winner when using ranked voting in a single-winner election. It is sometimes referred to as the Simpson–Kramer method, and the successive reversal method. Minimax selects as the winner the candidate whose greatest pairwise defeat is smaller than the greatest pairwise defeat of any other candidate: or, put another way, "the only candidate whose support never drops below [N] percent" in any pairwise contest, with N as high as possible.

Related publications (1)

Stripping the Discount Curve—A Robust Machine Learning Approach

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