Yield Curve Estimation by Kernel Smoothing Methods

By Oliver B. Linton, Enno Mammen, Jens Perch Nielsen, Carsten Tanggaard. 2001

85 citations recorded by the source corpus.

We introduce a new method for the estimation of discount functions, yield curves and forward curves from government issued coupon bonds. Our approach is nonparametric and does not assume a particular functional form for the discount function although we do show how to impose various restrictions in the estimation. Our method is based on kernel smoothing and is defined as the minimum of some localized population moment condition. The solution to the sample problem is not explicit and our estimation procedure is iterative, rather like the backfitting method of estimating additive nonparametric models. We establish the asymptotic normality of our methods using the asymptotic representation of our estimator as an infinite series with declining coefficients. The rate of convergence is standard for one dimensional nonparametric regression. We investigate the finite sample performance of our method, in comparison with other well-established methods, in a small simulation experiment.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/89e0b9666c02849c486042ef2b5c5e037dd1e2d1

Citations in the corpus

• Examining the Nelson-Siegel Class of Term Structure Models: In-Sample Fit versus Out-of-Sample Forecasting Performance (2007, unknown): “Other term structure estimation methods use for example cubic splines (McCulloch, 1975), exponential splines (Vasicek and Fong, 1982), polynomials functions (Chambers et al., 1984), parametric methods (Nelson-Siegel, see e.g. Bliss, 1997) or non-parametric methods (Linton et al., 2001).”