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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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
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