A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks
通过多种样本外预测标准（如均方误差、方向准确率、交易系统盈利性），检验远期利率能否预测未来即期利率，并评估人工神经网络模型及滚动窗口大小的影响。结果发现远期溢价有助于预测利率变化方向，但样本内施瓦茨信息准则不能可靠反映样本外表现。
We take a model-selection approach to the question of whether forward-interest rates are useful in predicting future spot rates, using a variety of out-of-sample forecast-based model-selection criteria—forecast mean squared error, forecast direction accuracy, and forecast-based trading-system profitability. We also examine the usefulness of a class of novel prediction models called artificial neural networks and investigate the issue of appropriate window sizes for rolling-window-based prediction methods. Results indicate that the premium of the forward rate over the spot rate helps to predict the sign of future changes in the interest rate. Furthermore, model selection based on an in-sample Schwarz information criterion (SIC) does not appear to be a reliable guide to out-of-sample performance in the case of short-term interest rates. Thus, the in-sample SIC apparently fails to offer a convenient shortcut to true out-of-sample performance measures.