An evaluation of alternative methods used
in the estimation of Gaussian term structure models
Published online: 18 August 2013
Ó Springer Science+Business Media New York 2013
Abstract This paper provides an evaluation of ﬁve methods, proposed in the literature,
for extracting factors used in the estimation of Gaussian afﬁne term structure models. We
assert that irrespective of the method used for extracting state variables, cross-sectional and
serial correlations exist in measurement errors. However, using a simulation design which
is consistent with the data, we show that parameter estimation using the Kalman ﬁlter and
the model-free method are quite precise in the presence of serial and cross-sectional
correlations in the error term, and, in the presence of different measurement errors, the
Kalman ﬁlter demonstrates strong empirical tractability.
Keywords Model evaluation Á Statistical simulation methods Á Financial
econometrics Á Model estimation Á Model construction
JEL Classiﬁcation C51 Á C15 Á C58 Á C52
Interest rate models are useful to researchers in many tasks such as the pricing of interest
rate derivatives (Hull and White 1990), interest rate risk management (Perignon et al.
2007), and understanding potential sources of crises (Taylor 2009). One of the most
popular classes of interest rate models is the family of afﬁne term structure models of
Dufﬁe and Kan (1996).
There are many studies on the term structure of interest rates
J. Juneja (&)
College of Business Administration, San Diego State University, San Diego, CA 92182, USA
Please see Yeh and Lin (2003) or Lundtofte (2013) for studies which implement interest rate models using
a framework that is different from Dufﬁe and Kan (1996). Lundtofte (2013) study interest rates using a
general equilibrium model set up that enables consumers to learn within ﬁnancial markets, while Yeh and
Lin (2003) employ general equilibrium models and curve-ﬁtting techniques (e.g. spline methods) to study
the Taiwanese government bond market.
Rev Quant Finan Acc (2015) 44:1–24