  Accurately fitting the term structure of interest rates is critical to
central banks and other market participants. The Nelson-Siegel and
Nelson-Siegel-Svensson models are probably the best-known models for this
purpose due to their intuitive appeal and simple representation. However, this
simplicity comes at a price. The difficulty in calibrating these models is
twofold. Firstly, the objective function being minimized during the calibration
procedure is nonlinear and has multiple local optima. Secondly, there is strong
co-dependence among the model parameters. As a result, their estimated values
behave erratically over time. To avoid these problems, we apply a heuristic
optimization method, specifically the Genetic Algorithm approach, and show that
it is able to construct reliable interest rate curves and stable model
parameters over time, regardless of the shape of the curves.
