We show that the difference between the natural rate of interest and the current level of monetary policy stance, which we label Convergence Gap (CG), contains information that is valuable for bond predictability. Adding CG in forecasting regressions of bond excess returns significantly raises...

Nominal yields can be expressed as the sum of an expectation, term premium, and convexity component, and in turn of their real and inflation counterparts. We extract these terms from the yield curve of the U.S., Euro Area, U.K., and Japan using a term structure model that explicitly captures the...

We incorporate a latent stochastic volatility factor and macroeconomic expectations in an affine model for the term structure of nominal and real rates. We estimate the model over 1999-2016 on U.S. data for nominal and TIPS yields, the realized and implied volatility of T-bonds and survey...

Long term forward rates contain information that greatly improves the precision with which expectations of future short rates can be distinguished from risk premia in the term structure. Indeed, in affine models, the slope of the term structure of risk premia for long maturities is very closely...

We show that machine learning methods, in particular extreme trees and neural networks (NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts based on macroeconomic and yield information translate into economic gains that are larger than those obtained...

We show that a business-cycle component of consumption growth (dubbed business-cycle consumption) with cycles between 2 and 4 years is effective in explaining the differences in risk premia across alternative test assets, including recently-proposed anomaly portfolios. We formalize the mapping...