A New Linear Estimator for Gaussian Dynamic Term Structure Models

Antonio Diez de los Rios

Staff Working Papers from Bank of Canada

Abstract: This paper proposes a novel regression-based approach to the estimation of Gaussian dynamic term structure models that avoids numerical optimization. This new estimator is an asymptotic least squares estimator defined by the no-arbitrage conditions upon which these models are built. We discuss some efficiency considerations of this estimator, and show that it is asymptotically equivalent to maximum likelihood estimation. Further, we note that our estimator remains easy-to-compute and asymptotically efficient in a variety of situations in which other recently proposed approaches lose their tractability. We provide an empirical application in the context of the Canadian bond market.

Keywords: Asset Pricing; Econometric and statistical methods; Interest rates (search for similar items in EconPapers)
JEL-codes: C13 E43 G12 (search for similar items in EconPapers)
Pages: 63 pages
Date: 2013
New Economics Papers: this item is included in nep-ecm, nep-ias, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Journal Article: A New Linear Estimator for Gaussian Dynamic Term Structure Models (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:13-10

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