What does the Yield Curve imply about Investor Expectations?

We find that investors' expectations of U.S. nominal yields, at different maturities and forecast horizons, exhibit significant time-variation during the Great Moderation. Nominal zero-coupon bond yields for the U.S. are used to fit the yield curve using a latent factor model. In the benchmark model, the VAR process used to characterize the conditional forecasts of yields has constant coefficients. The alternative class of models assume that investors use adaptive learning, in the form of a constant gain algorithm and different endogenous gain algorithms, which we propose here. Our results indicate that incorporating time-varying coefficients in the conditional forecasts of yields lead to large improvements in forecasting performance, at different maturities and horizons. These improvements are even more substantial during the Great Recession. We conclude that our results provide strong empirical motivation to use the class of adaptive learning models considered here, for modeling potential investor expectation formation in periods of low and high volatility, and the endogenous learning model leads to significant improvements over the benchmark in periods of high volatility. A policy experiment, which simulates a surprise shock to the level of the yield curve, illustrates that the conditional forecasts of yields implied by the learning models do significantly better at capturing the response observed in the realized yield curve, relative to the constant-coefficients model. Furthermore, the endogenous learning algorithm does well at matching the time-series patterns observed in expected excess returns implied by the Survey of Professional Forecasters.