We compare the differences between the Chinese and U.S. bond risk premia. We find that the expectations hypothesis fails in the two bond markets: We identify the Chinese and U.S. bond time-varying risk premia by forecasting the corresponding excess return of n-year bond using the n-year forward rate and n-year forward spread, respectively. To focus on the systematical forecasts, we then combine the forward rates at different maturities as the return-forecast factors. Unlike the one-factor model introduced by Cochrane and Piazzesi (2005), a two-factor model including level- and slope-based factors explains significantly Chinese bond premia with R2 up to 68%. More importantly, the slope-based factor sharply improves the performance of test. The results are robust with respect to measurement errors, multicollinearity and small-sample biases. Out-of-sample tests show that, in recent years, the U.S. bond market changes drastically, and tends to be like the Chinese market. We use the empirical results to calibrate the parameters of affine model, and find that the differences of bond premia between the two markets are caused by the differences of dynamics of state variables and risk attitude of investor.

Most existing empirical studies on affine term structure models have primarily focused on in-sample Þt of historical bond yields and ignored out-of-sample forecast of future bond yields. Using 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 term structure 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 simple random walk models in density forecast. Our analysis demonstrates the great potential of affine models for financial risk management in fixed-income markets.

This paper investigates a two-factor affine model for the credit spreads on corporate bonds. The Þrst factor can be interpreted as the level of the spread, and the second factor is the volatility of the spread. The riskless interest rate is modeled using a standard two-factor affine model, thus leading to a four-factor model for corporate yields. This approach allows us to model the volatility of corporate credit spreads as stochastic, and also allows us to capture higher moments of credit spreads. We use an extended Kalman Þlter approach to estimate our model on corporate bond prices for 108 Þrms. The model is found to be successful at Þtting actual corporate bond credit spreads, resulting in a signiÞcantly lower root mean square error (RMSE) than a standard alternative model in both in-sample and out-of-sample analyses. In addition,key properties of actual credit spreads are better captured by the model.