Forecasting Robust Bond Risk Premia using Technical Indicators

Extensive research has been done on the relevance of economic variables in predicting bond returns. However, only few studies have considered the importance of technical indicators which are commonly used by investors and traders. This paper studies the predictive power of technical indicators in forecasting bond risk premia. Particularly, this paper assesses the robustness of the indicators by considering several small-sample size problems such as standard error bias and overlapping return bias which frequently appear in predictive regressions for bond returns. For this purpose, I use a bootstrap procedure proposed by Bauer and Hamilton (2018) and a formal test developed by Ibragimov and Müller (2010). I find that technical indicators are not always significant and robust predictors. Only the level and slope of the yield curve consistently show to be significant in forecasting robust bond risk premia. From an asset allocation perspective, a portfolio constructed with the first five principal components of yields and the technical indicators generates the most utility gains.