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Estimating the Yield Curve Using Calibrated Radial Basis Function Networks

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3497))

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Abstract

Nonparametric approaches of estimating the yield curve have been widely used as alternative approaches that supplement parametric approaches. In this paper, we propose a novel yield curve estimating algorithm based on radial basis function networks, which is a nonparametric approach. The proposed method is devised to improve accuracy and smoothness of the fitted curve. Numerical experiments are conducted for 57 U.S. Treasury securities with different maturities and demonstrate a significant performance improvement to reduce test error compared to other existing algorithms.

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© 2005 Springer-Verlag Berlin Heidelberg

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Han, G., Lee, D., Lee, J. (2005). Estimating the Yield Curve Using Calibrated Radial Basis Function Networks. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_142

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  • DOI: https://doi.org/10.1007/11427445_142

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25913-8

  • Online ISBN: 978-3-540-32067-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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