Estimating the yield curve using calibrated radial basis function networks

Gyusik Han; Daewon Lee; Jaewook Lee

doi:10.1007/11427445_142

Estimating the yield curve using calibrated radial basis function networks

Gyusik Han^*
, Daewon Lee
, Jaewook Lee

^*Corresponding author for this work

Department of Business Administration

Pohang University of Science and Technology

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

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.

Original language	English
Pages (from-to)	885-890
Number of pages	6
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3497
Issue number	II
DOIs	https://doi.org/10.1007/11427445_142
State	Published - 2005
Event	Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 - Chongqing, China Duration: 2005.05.30 → 2005.06.1

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10.1007/11427445_142

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title = "Estimating the yield curve using calibrated radial basis function networks",

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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year = "2005",

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journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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number = "II",

note = "Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 ; Conference date: 30-05-2005 Through 01-06-2005",

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Estimating the yield curve using calibrated radial basis function networks. / Han, Gyusik; Lee, Daewon; Lee, Jaewook.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3497, No. II, 2005, p. 885-890.

Research output: Contribution to journal › Conference article › peer-review

TY - JOUR

T1 - Estimating the yield curve using calibrated radial basis function networks

AU - Han, Gyusik

AU - Lee, Daewon

AU - Lee, Jaewook

PY - 2005

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N2 - 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.

AB - 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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DO - 10.1007/11427445_142

M3 - Conference article

AN - SCOPUS:24944582639

SN - 0302-9743

VL - 3497

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EP - 890

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

IS - II

T2 - Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005

Y2 - 30 May 2005 through 1 June 2005

ER -

Estimating the yield curve using calibrated radial basis function networks

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