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http://dx.doi.org/10.5351/KJAS.2008.21.1.159

Estimating the Term Structure of Interest Rates Using Mixture of Weighted Least Squares Support Vector Machines  

Nau, Sung-Kyun (Division of Information and Computer Science, Dankook University)
Shim, Joo-Yong (Dept. of Applied Statistics, Catholic University of Daegu)
Hwang, Chang-Ha (Division of Information and Computer Science, Dankook University)
Publication Information
The Korean Journal of Applied Statistics / v.21, no.1, 2008 , pp. 159-168 More about this Journal
Abstract
Since the term structure of interest rates (TSIR) has longitudinal data, we should consider as input variables both time left to maturity and time simultaneously to get a more useful and more efficient function estimation. However, since the resulting data set becomes very large, we need to develop a fast and reliable estimation method for large data set. Furthermore, it tends to overestimate TSIR because data are correlated. To solve these problems we propose a mixture of weighted least squares support vector machines. We recognize that the estimate is well smoothed and well explains effects of the third stock market crash in USA through applying the proposed method to the US Treasury bonds data.
Keywords
Mixture; support vector machine; term structure of interest rates; weighted least squares support vector machine; yield curve;
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