Stripping the Swiss discount curve using kernel ridge regression

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      <subfield code="a">Stripping the Swiss discount curve using kernel ridge regression</subfield>
      <subfield code="c">Nicolas Camenzind & Damir Filipovic</subfield>
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      <subfield code="a">We analyze and implement the kernel ridge regression (KR) method developed in Filipovic et al. (Stripping the discount curvea robust machine learning approach. Swiss Finance Institute Research Paper No. 2224.  to estimate the risk-free discount curve for the Swiss government bond market. We show that the insurance industry standard SmithWilson 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 20102022 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</subfield>
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      <subfield code="g">15/08/2024 Volumen 14 - Número 2 - agosto 2024 , p.371-410</subfield>
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