Research of the term structure of interest rates based on improved J-LSSVR

摘要：

Abstrac In this paper, Least Squares Support Vector Regression (LSSVR) method is improved to have a sparse, and enhance the generalization ability of the method. In LSSVR model, an increase of three indexes set down by certain criteria to determine the selection and support vector. Then two models were selected to fit the sample data on the bond spot yield curve. We can derived from analysis and comparison revealed that the improved model fit residuals and LSSVR model results broadly consistent with their training time and prediction time of a relatively large Shortened. Thus, we can set through the addition of three indexes to increase the generalization ability and reduce the computing time when using support vector machine to fit the term structure of interest rates. t

关键词： Support vector regression Term structure of interest rates Non-parametric method

作者: Cunhou Liu Binbin Chen Rongxi Zhou

作者单位: School of Economics and Management, Beijing University of Chemical Technology, Beijing, China

会议类型: 国际会议

会议名称: 2011 Fourth International Conference on Business Intelligence and Financial Engineering(第四届商务智能与金融工程国际会议 BIFE2011)

会议地点: 武汉

会议语种:英文

页码: 434-438

在线出版日期: 2011-10-17（万方平台首次上网日期，不代表论文的发表时间）