Stripping the Discount Curve - a Robust Machine Learning Approach

Damir Filipović, Markus Pelger and Ye Ye
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Damir Filipović: Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute
Ye Ye: Stanford University

No 22-24, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: We introduce a robust, flexible and easy-to-implement method for estimating the yield curve from Treasury securities. This method is non-parametric and optimally learns basis functions in reproducing Hilbert spaces with an economically motivated smoothness reward. We provide a closed-form solution of our machine learning estimator as a simple kernel ridge regression, which is straightforward and fast to implement. We show in an extensive empirical study on U.S. Treasury securities, that our method strongly dominates all parametric and non-parametric benchmarks. Our method 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.

Keywords: yield curve estimation; U.S. Treasury securities; term structure of interest rates; nonparametric method; machine learning in finance; reproducing kernel Hilbert space (search for similar items in EconPapers)
JEL-codes: C14 C38 C55 E43 G12 (search for similar items in EconPapers)
Pages: 73 pages
Date: 2022-03
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-ecm, nep-mac and nep-ore
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2224

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