The Semiparametric Model of Interest Rate Term Structure Based on GCV Method and Its Empirical Comparison

摘要：

In order to improve the smoothness of curve fitted by the interest rate term structure model of polynomial spline functions, the adaptive semiparametric regression with a penalized item is introduced to estimate the unknown parameters. The generalized cross-validation method is discussed to select the smoothing parameter, and genetic algorithm is applied to search the optimal smoothing parameter. Then, the empirical results show that this model with penalty function is relatively effective in China. However, the curve fitting smoothness is improved to some extend at the expense of fitting accuracy.

关键词： term structure of interest rate penalty function generalized cross-validation genetic algorithm

作者: Shuyi Ren Fengmei Yang Rongxi Zhou

作者单位: College of Science, Beijing University of Chemical Technology, Beijing, 100029, China School of Economics and Management,Beijing University of Chemical Technology,Beijing, 100029, China

会议类型: 国际会议

会议名称: The Fourth International Joint Conference on Computational Science and Optimization(第四届计算科学与优化国际大会 CSO 2011)

会议地点: 昆明、丽江

会议语种:英文

页码: 210-214

在线出版日期: 2011-04-15（万方平台首次上网日期，不代表论文的发表时间）