Stripping the Swiss Discount Curve using Kernel Ridge Regression

Abstract

We analyze and implement the kernel ridge regression (KR) method developed in [FPY22] to estimate the risk-free discount curve for the Swiss government bond market. We show that the insurance industry standard Smith–Wilson method is a special case of the KR framework. We recapitulate the curve estimation methods of the Swiss Solvency Test (SST) and the Swiss National Bank (SNB). In an extensive empirical study covering the years 2010 to 2022 we compare the KR curves with the SST and SNB curves. The KR method proves to be robust, flexible, transparent, reproducible and easy to implement, and outperforms the benchmarks in- and out-of-sample. We show the limitations of all methods for extrapolating the yield curve and propose possible solutions for the extrapolation problem. We conclude that the KR method is the preferred method for estimating the discount curve.

Keywords: Yield curve estimation, Swiss government bond market, Smith–Wilson method, Swiss Solvency Test, Swiss National Bank, machine learning in finance, reproducing kernel Hilbert space

JEL Classification: C14, C55, E43, E52, G12, G22