Prediction Bias Correction for Dynamic Term Structure Models

When the yield curve is modelled using an affine factor model, residuals may still contain relevant information and do not adhere to the familiar white noise assumption.This paper proposes a pragmatic way to improve out of sample performance for yield curve forecasting. The proposed adjustment is illustrated via a pseudo out-of-sample forecasting exercise implementing the widely used Dynamic Nelson Siegel model. Large improvement in forecasting performance is achieved throughout the curve for different forecasting horizons. Results are robust to different time periods, as well as to different model specifications.