Using the daily data of Chinese 7-day repo rates from January 1, 1997 to December 31, 2008, this paper tests a variety of popular spot rate models, including single-factor diffusion, GARCH, Markov regime-switching and jump-diffusion models. We document that Chinese spot rates are subject to both market forces and administrative forces. GARCH, regime-switching and jump-diffusion models capture some important features of the dynamics of Chinese spot rates, but all models under study are overwhelmingly rejected. We further explore possible sources of model misspecification using diagnostic tests.

We provide a comprehensive analysis of the out-of-sample performance of a wide variety of spot rate models in forecasting the probability density of future interest rates. While the most parsimonious models perform best in forecasting the conditional mean of many financial time series, we find that the spot rate models that incorporate conditional heteroskedasticity and excess kurtosis or heavy-tails have better density forecasts. GARCH significantly improves the modeling of the conditional variance and kurtosis, while regime switching and jumps improve the modeling of the marginal density of interest rates. Our analysis shows that the sophisticated spot rate models in the existing literature are important for applications involving density forecasts of interest rates.