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research article

Stripping the Swiss discount curve using kernel ridge regression

Camenzind, Nicolas  
•
Filipovic, Damir  
June 7, 2024
European Actuarial Journal

We analyze and implement the kernel ridge regression (KR) method developed in Filipovic et al. (Stripping the discount curve-a robust machine learning approach. Swiss Finance Institute Research Paper No. 22-24. SSRN. https://ssrn.com/abstract=4058150, 2022) 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 2010-2022 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.

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Type
research article
DOI
10.1007/s13385-024-00386-4
Web of Science ID

WOS:001242315800001

Author(s)
Camenzind, Nicolas  
Filipovic, Damir  
Date Issued

2024-06-07

Publisher

Springer Heidelberg

Published in
European Actuarial Journal
Subjects

Physical Sciences

•

Yield Curve Estimation

•

Swiss Government Bond Market

•

Smith-Wilson Method

•

Swiss Solvency Test

•

Swiss National Bank

•

Machine Learning In Finance

•

Reproducing Kernel Hilbert Space

•

C14

•

C55

•

E43

•

E52

•

G12

•

G22

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
CSF  
FunderGrant Number

EPFL Lausanne

Available on Infoscience
July 3, 2024
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/208999
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