跨债种迁移学习，用核方法提升债券曲线估计精度与稳定性
Transfer Learning Across Fixed-Income Product Classes
- 将核岭回归拓展至向量值空间，统一建模多债种贴现曲线
- 引入经济合理性正则项，使不同债种间利差曲线更平滑，提升外推性能
- 提供高斯过程解释，可量化预测不确定性，适合金融建模与风控场景
我们提出一种跨固定收益产品类别的贴现曲线迁移学习框架。针对稀疏或噪声数据下贴现曲线估计困难的问题，将核岭回归（KR）扩展至向量值设置，在向量值再生核希尔伯特空间（RKHS）中构建凸优化问题，每个解分量对应特定产品类别的隐含贴现曲线。引入基于经济原理的额外正则化项，促进不同产品类别间利差曲线的平滑性，并证明其导致可分离核结构。主要理论贡献是分解了由可分离核诱导的向量值RKHS范数。进一步提供了向量值KR的高斯过程解释，实现估计不确定性的量化。示例表明，相比单曲线估计，迁移学习显著缩小置信区间；大规模遮蔽实验显示，迁移学习显著提升外推性能。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。