Transfer Learning Across Fixed-Income Product Classes

Nicolas Camenzind and Damir Filipović
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Nicolas Camenzind: Swiss Federal Institute of Technology in Lausanne -EPFL
Damir Filipović: École Polytechnique Fédérale de Lausanne (EPFL); Swiss Finance Institute

No 25-50, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: We propose a framework for transfer learning of discount curves across different fixed-income product classes. Motivated by challenges in estimating discount curves from sparse or noisy data, we extend kernel ridge regression (KR) to a vector-valued setting, formulating a convex optimization problem in a vector-valued reproducing kernel Hilbert space (RKHS). Each component of the solution corresponds to the discount curve implied by a specific product class. We introduce an additional regularization term motivated by economic principles, promoting smoothness of spread curves between product classes, and show that it leads to a valid separable kernel structure. A main theoretical contribution is a decomposition of the vector-valued RKHS norm induced by separable kernels. We further provide a Gaussian process interpretation of vector-valued KR, enabling quantification of estimation uncertainty. Illustrative examples demonstrate that transfer learning significantly improves extrapolation performance and tightens confidence intervals compared to single-curve estimation.

Keywords: yield curve estimation; transfer learning; nonparametric estimator; machine learning in finance; vector-valued reproducing kernel Hilbert space (search for similar items in EconPapers)
JEL-codes: C14 E43 G12 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2025-05
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2550

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