Forecasting Bond Risk Premia using Stationary Yield Factors

Abstract

The standard way to summarize the yield curve is to use the first three principal components of the yield curve, resulting in level, slope and curvature factors. Yields, however, are non-stationary. We analyze the first three principal components of yield changes, which correspond to changes in level, slope and curvature. The new factors based on changes in yields have strong predictive power for bond risk premia, in contrast to the factors based on yield levels. We also provide insights into the impact this has on the added value of macro data for bond risk premia predictions and the recent conclusion that machine learning provides better forecasts than linear regression.

Keywords: Yield curve, Bond risk premia, Forecasting, PCA, Machine learning

JEL Classification: G12, G17, C38, E43, C45