Specification Analysis of Affine Term Structure Models

53 Pages Posted: 10 Jul 2000 Last revised: 1 Oct 2022

See all articles by Qiang Dai

Qiang Dai

University of North Carolina (UNC) at Chapel Hill - Finance Area

Kenneth J. Singleton

Stanford University - Graduate School of Business

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Date Written: August 1997

Abstract

This paper characterizes, interprets, and tests the over-identifying restrictions imposed in affine models of the term" structure. Letting r(t) = ë Y(t), where Y is an unobserved vector affine process, our analysis proceeds in three steps. First, we show that affine models can be categorized according to the different over-identifying restrictions they impose on (i) ë, and (ii) the parameters of the diffusion matrices. Second, this formulation is shown to be equivalent to a model in which there is a terraced drift structure with one of the state variables being the stochastic long-run mean of r. This equivalence allows direct comparisons of the substantive restrictions on the dynamics of interest rates imposed in CIR-style models and models in which the state variables are the stochastic long-run mean and volatility of r. Third, we compute simulated method of moments estimates of a three-factor affine term structure model, and test the over-identifying restrictions on the joint distribution of long- and short-term interest rates implied by extant affine models of r. We find allowing for correlated factors is key to simultaneously describing the short and long ends of the yield curve. This finding is interpreted in terms of the properties of the risk factors underlying term structure movements.

Suggested Citation

Dai, Qiang and Singleton, Kenneth J., Specification Analysis of Affine Term Structure Models (August 1997). NBER Working Paper No. w6128, Available at SSRN: https://ssrn.com/abstract=225893

Qiang Dai (Contact Author)

University of North Carolina (UNC) at Chapel Hill - Finance Area ( email )

Kenan-Flagler Business School
Chapel Hill, NC 27599-3490
United States
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Kenneth J. Singleton

Stanford University - Graduate School of Business ( email )

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