Estimating the yield curve using calibrated radial basis function networks
SCIE
SCOPUS
- Title
- Estimating the yield curve using calibrated radial basis function networks
- Authors
- Han, G; Lee, D; Lee, J
- Date Issued
- 2005-01
- Publisher
- SPRINGER-VERLAG BERLIN
- 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.
- Keywords
- NEURAL-NETWORK
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/24515
- DOI
- 10.1007/11427445_142
- ISSN
- 0302-9743
- Article Type
- Article
- Citation
- LECTURE NOTES IN COMPUTER SCIENCE, vol. 3497, page. 885 - 890, 2005-01
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