Transfer Learning Across Fixed-Income Product Classes

TL;DR

This paper develops a transfer learning framework for discount curves across fixed-income products using vector-valued kernel ridge regression, incorporating economic regularization, and demonstrates improved estimation and extrapolation performance.

Contribution

It introduces a novel transfer learning approach with a regularized vector-valued kernel method for discount curves, including theoretical analysis and uncertainty quantification.

Findings

Transfer learning tightens confidence intervals.

Significant improvement in extrapolation performance.

Theoretical decomposition of RKHS norm for vector-valued kernels.

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 show how transfer learning tightens confidence intervals compared to single-curve estimation. An extensive masking experiment demonstrates that transfer learning significantly improves extrapolation performance.