Gaussian Process Mixture Based Prediction of Treasury Bond Yield Rate

The key problem of bond research is how to find the relationship between repayment maturity and yield to maturity, known as the term structure of interest rates. Among all the term structures of interest rates, the most important one is the term structure of the interest rates of treasury bond. Generally, there are two methods using economic theoretical model and quantitative model, respectively, for investigating the curve structure of interest rate, however, they are not so effective to predict the rate of treasure bond yield. With the help of the strong data fitting ability of the mixture of Gaussian processes(MGP) model, we apply it to the forecast of the rate of the treasury bound or national debt yield in this paper. Specifically, we consider the treasury bond yield rate as the output variable, screen out the most important factors as the input or driving variable, and utilize the MGP model to learn and fit the given data for the prediction of the rate of treasury bond yield. It is demonstrated by the experimental results that the MGP model can better describe the term structure of the interest rates of treasury bond. Moreover, in comparison with the other machine learning models, the MGP model can achieve the better prediction result on the test data of bond yield.