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中国管理科学 ›› 2015, Vol. 23 ›› Issue (2): 29-38.doi: 10.16381/j.cnki.issn1003-207x.2015.02.004

• 论文 • 上一篇    下一篇

中国利率期限结构的理论与实证研究——基于无套利DNS模型和DNS模型

葛静, 田新时   

  1. 华中科技大学经济学院, 湖北 武汉 430074
  • 收稿日期:2013-03-04 修回日期:2013-06-30 出版日期:2015-02-20 发布日期:2015-02-28
  • 作者简介:葛静(1981-),男(汉族),湖北荆州人,华中科技大学经济学院博士研究生,研究方向:金融风险管理.

Models and Empirical Research of Term Structure of Interest Rates in China: Based on No-Arbitrage DNS Model and DNS Model

GE Jing, TIAN Xin-shi   

  1. School of Economics HUST, Wuhan 430074, China
  • Received:2013-03-04 Revised:2013-06-30 Online:2015-02-20 Published:2015-02-28

摘要: 随着中国利率市场化进程的推进和利率衍生产品的丰富,利率作为证券定价的核心变量之一,如何对其进行有效预测成为资产定价的关键。无套利DNS利率模型是在DNS模型的基础上发展而来,理论研究表明它是受约束的高斯仿射过程。本文以2005-2012年上交所国债价格月度数据隐含的利率期限结构为样本,利用准极大似然法对无套利DNS模型和DNS模型进行估计,研究结果表明:无套利DNS模型兼顾了仿射利率模型的无套利条件和DNS模型实证效果;它保持了DNS模型的预测优势,所有期限的预测误差都在1.5个基点以内;模型常数调整项的特征是随着利率期限的延长,调整力度逐步增强。

关键词: 无套利DNS模型, DNS模型, 期限结构, 仿射因子模型

Abstract: With the deep development of Chinese interest rate marketization and the diversification of the interest rate derivatives,how to predict interest rate effectively,as one of the kernel variables in securities pricing, has become a key work. The no-arbitrage DNS model is developed on the basis of DNS model, and theoretical studies show that it is a constrained affine Gauss process. With the term structure of yields implied in monthly bonds price from 2005 to 2012 in Shanghai Stock Exchange (SSE), the no-arbitrage DNS model and the DNS model are studied empirically using the method of quasi-maximum likelihood. Are the results show that the no-arbitrage DNS model keeps the no arbitrage condition of affine term structure model and good empirical results of DNS model; and also it keeps the forecast advantage of DNS model, because prediction errors of all term structures in both two models are within 1.5 basis points; and also the characteristics of constant adjustment item show that the value is increasing with the extension of maturities.

Key words: no-arbitrage DNS model, DNS model, term structure, affine factor model

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