预测中国公司债券的收益率

Maojun Zhang, Hao Li
{"title":"预测中国公司债券的收益率","authors":"Maojun Zhang, Hao Li","doi":"10.1504/ijcse.2020.10031601","DOIUrl":null,"url":null,"abstract":"In this paper we focus on predicting the yield that is the centrepiece of bond markets. The dynamic Nelson-Siegel model is used to predict the yield of the Chinese corporate bonds with a class of AA, AA+ and AAA ratings. Our empirical results show that this model not only provides good in-sample fit, but also indicates the long-term, medium-term and short-term dynamic features of the yield curve of the corporate bonds with different credit ratings. Finally, we employ AR(1) model to forecast the three factors of the yield curve. Overall, the outcomes are very encouraging for the development of better forecasting systems for fixed income markets.","PeriodicalId":340410,"journal":{"name":"Int. J. Comput. Sci. Eng.","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Forecasting the yield of Chinese corporate bonds\",\"authors\":\"Maojun Zhang, Hao Li\",\"doi\":\"10.1504/ijcse.2020.10031601\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we focus on predicting the yield that is the centrepiece of bond markets. The dynamic Nelson-Siegel model is used to predict the yield of the Chinese corporate bonds with a class of AA, AA+ and AAA ratings. Our empirical results show that this model not only provides good in-sample fit, but also indicates the long-term, medium-term and short-term dynamic features of the yield curve of the corporate bonds with different credit ratings. Finally, we employ AR(1) model to forecast the three factors of the yield curve. Overall, the outcomes are very encouraging for the development of better forecasting systems for fixed income markets.\",\"PeriodicalId\":340410,\"journal\":{\"name\":\"Int. J. Comput. Sci. Eng.\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Comput. Sci. Eng.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/ijcse.2020.10031601\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Comput. Sci. Eng.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijcse.2020.10031601","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在本文中,我们着重于预测债券市场的核心——收益率。采用动态Nelson-Siegel模型对中国AA、AA+和AAA级公司债的收益率进行了预测。实证结果表明,该模型不仅具有良好的样本内拟合效果,而且能够较好地反映不同信用等级公司债券收益率曲线的长、中、短期动态特征。最后,采用AR(1)模型对收益率曲线的三个因子进行预测。总体而言,这些结果对于开发更好的固定收益市场预测系统非常鼓舞人心。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting the yield of Chinese corporate bonds
In this paper we focus on predicting the yield that is the centrepiece of bond markets. The dynamic Nelson-Siegel model is used to predict the yield of the Chinese corporate bonds with a class of AA, AA+ and AAA ratings. Our empirical results show that this model not only provides good in-sample fit, but also indicates the long-term, medium-term and short-term dynamic features of the yield curve of the corporate bonds with different credit ratings. Finally, we employ AR(1) model to forecast the three factors of the yield curve. Overall, the outcomes are very encouraging for the development of better forecasting systems for fixed income markets.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信