Out-of-Sample Performance of Discrete-Time Spot Interest Rate Models
系统评估了多种即期利率模型在预测未来利率概率密度方面的样本外表现，发现包含条件异方差和厚尾特征的模型密度预测更优，对利率相关应用有重要参考价值。
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. Although 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 heteroscedasticity and excess kurtosis or heavy tails have better density forecasts. Generalized autoregressive conditional heteroscedasticity significantly improves the modeling of the conditional variance and kurtosis, whereas 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.