Estimating and testing exponential-affine term structure models by Kalman filter

被引：28

作者：

Duan J.-C. ^{[1
]}

Simonato J.-G. ^{[2
]}

机构：

[1] Hong Kong University of Science and Technology, Hong Kong, Clear Water Bay

来源：

Review of Quantitative Finance and Accounting | 1999年 / 13卷 / 2期

关键词：

Exponential-affine; Kalman filter; Quasi-maximum likelihood; State-space model; Term structure;

D O I：

10.1023/A:1008304625054

中图分类号：

学科分类号：

摘要：

This paper proposes a unified state-space formulation for parameter estimation of exponential-affine term structure models. The proposed method uses an approximate linear Kalman filter which only requires specifying the conditional mean and variance of the system in an approximate sense. The method allows for measurement errors in the observed yields to maturity, and can simultaneously deal with many yields on bonds with different maturities. An empirical analysis of two special cases of this general class of model is carried out: the Gaussian case (Vasicek 1977) and the non-Gaussian case (Cox Ingersoll and Ross 1985 and Chen and Scott 1992). Our test results indicate a strong rejection of these two cases. A Monte Carlo study indicates that the procedure is reliable for moderate sample sizes. © 1999 Kluwer Academic Publishers,.

引用

页码：111 / 135

页数：24

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