文章基本信息

标题：CALIBRATING THE NELSON-SIEGEL MODEL CLASSES AND THEIR ESTIMATION USING HYBRID-GENETIC ALGORITHM APPROACH: CASE STUDY OF INDONESIAN GOVERNMENT BONDS
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作者：MUSLIM ; DEDI ROSADI ; GUNARDI 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2019
卷号：97
期号：2
页码：409-422
出版社：Journal of Theoretical and Applied
摘要：In this paper, we consider the problem of modelling the yield curve using Nelson-Siegel model classes. Nelson-Siegel model classes discussed here are NS model, BL model, NSS model, RF model, and our proposed NSSE models. NSSE model is a model which extends the standard NS model as Nelson-Siegel model class by adding some linear and non-linear parameters in which form the fourth hump of the model class. The purpose of adding the hump is to accommodate the possibility of having the following cases: the first, the condition when the short term and the medium term yields are higher than the long term yield. The second, the condition when the upper-value short term yields are higher than both the short term yields on average and the long term yields. The third, the case when the upper-value medium term yields are higher than both the medium term yields on average and the long term yields. These considered cases make the yield curve more likely to have minimum locals and therefore, the Nelson-Siegel model classes become more difficult to be estimated. To overcome this problem, in this paper we estimate the model using the hybrid-genetic algorithm approach and compare it with the standard estimation based on NLS method. We provide an empirical study using Indonesian Government-Bond Yield Curve (IGYC) data, and found that the best model for IGYC is 6-factors model.
关键词：Yield Curve; Nelson-Siegel Model; Hybrid Method; Genetic Algorithm; Nonlinear Least Square; and Constrained Optimization