Estimation of Dynamic Term Structure Models

Greg Duffee and Richard H. Stanton ()
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Richard H. Stanton: Haas School of Business, University of California, Berkeley, USA

Quarterly Journal of Finance (QJF), 2012, vol. 02, issue 02, 1-51

Abstract: We study the finite-sample properties of some of the standard techniques used to estimate modern term structure models. For sample sizes and models similar to those used in most empirical work, we reach three surprising conclusions. First, while maximum likelihood works well for simple models, it produces strongly biased parameter estimates when the model includes a flexible specification of the dynamics of interest rate risk. Second, despite having the same asymptotic efficiency as maximum likelihood, the small-sample performance of Efficient Method of Moments (a commonly used method for estimating complicated models) is unacceptable even in the simplest term structure settings. Third, the linearized Kalman filter is a tractable and reasonably accurate estimation technique, which we recommend in settings where maximum likelihood is impractical.

Keywords: No-arbitrage models; Efficient Method of Moments; dynamic risk premia; simulation inference; Kalman filter (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (35)

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DOI: 10.1142/S2010139212500085

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