Stripping the Swiss discount curve using kernel ridge regression

Author:

Camenzind Nicolas,Filipović Damir

Abstract

AbstractWe 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.

Funder

EPFL Lausanne

Publisher

Springer Science and Business Media LLC

Reference29 articles.

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2. BIS (2005) Zero-coupon yield curves: technical documentation. BIS Papers 25, Bank for International Settlements. https://www.bis.org/publ/bppdf/bispap25.htm

3. Camenzind N, Filipović D, Pelger M, Wang R (2023) Joint learning of international yield curves. In: Presented at SIAM conference on financial mathematics and engineering

4. Christensen JHE, Mirkov N (2022) The safety premium of safe assets. Working Paper 2019-28. Federal Reserve Bank of San Francisco. https://doi.org/10.24148/wp2019-28

5. EIOPA (2021) Technical documentation of the methodology to derive EIOPA’s risk-free interest rate term structures. Technical report, European Insurance and Occupational Pensions Authority. https://www.eiopa.europa.eu/tools-and-data/risk-free-interest-rate-term-structures_en

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