Using genetic algorithms to improve the term structure of interest rates fitting

The termstructure of interest rates is an instrument that gives us the necessary information for valueing deterministic financial cash flows, measuring the economic market expectations and testing the effectiveness of monetary policy decissions. However, it is not directly observable and needs to be measured by smoothing data obtained from asset prices through statistical techniques. Adjusting parsimonious functional forms - as proposed by Nelson and Siegel (1987) and Svensson (1994) - is the most popular technique. This method is based on bond yields to maturity and the high degree of non-linearity of the functions to be optimised make it very sensitive to the initial values omployed. In this context, this paper proposes the use og genetic algorithms to find these values and reduce the risk of false convergence, showing that stable time series parameters are obtained without the need to impose any kind of restrictions