Forecasting the Joint Probability Density of Bond Yields: Can Affine Models Beat Random Walk?

Abstract

The numerous empirical studies on affine term structure models have primarily focused on the in-sample fit of historical bond yields and ignored the out-of-sample forecast of future bond yields. Based on an omnibus nonparametric procedure for density forecast evaluation developed in this paper, we provide probably the first comprehensive empirical analysis of the out-of-sample performance of affine models in forecasting the joint conditional probability density of bond yields. We show that although it is difficult to forecast the conditional mean of bond yields, some affine models have good forecasts of the joint conditional density of bond yields and they significantly outperform the simple random walk models in density forecast. Our analysis demonstrates the great potential of affine models for financial risk management in fixed-income markets.