A Combinatorial Optimization Model of Interest Rate Term Structure Using Genetic Algorithms

In this paper, we develop a new term structure model of interest rates with combinatorial optimization method based on four classical models: polynomial spline model, exponential spline model, Nelson-Siegel model and Svensson model. Genetic algorithms are employed to solve the combinatorial optimization model. Then, we make some empirical comparisons of five models using daily bond data from Shanghai Stock Exchange in China covering the years from 2004 to 2009. The results show that the combinatorial optimization model outperforms the other models in most of the statistical indicators. Besides, the combinatorial model has good adaptability and robustness which are applicable in Chinese bond market.