Trend and Cycle in Bond Premia

Abstract

Common statistical measures of bond risk premia are volatile and countercyclical. This paper uses survey data on interest rate forecasts to construct subjective bond risk premia. Subjective premia are less volatile and not very cyclical; instead they are high, only around the early 1980s. The reason for the discrepancy is that survey forecasts of interest rates are made as if both the level and the slope of the yield curve are more persistent than under common statistical models. The paper then proposes a consumption based asset pricing model with learning to explain jointly the difference between survey and statistical forecasts, and the evolution of subjective premia. Adaptive learning gives rise to inertia in forecasts, as well as changes in conditional volatility that help understand both features.

Nonfinancial Firms as Cross‐Market Arbitrageurs

“…To measure investor sentiment, I use two strategies to capture departures from frictionless markets. One strategy draws on expectations data and deviations of investor expectations from the rational benchmark, building on a growing literature on investor expectations (Amromin and Sharpe (2013), Greenwood and Shleifer (2014), Piazzesi, Salomao, and Schneider (2015), Cieslak (2018)). The other strategy examines deviations from the law of one price (Lee, Shleifer, and Thaler (1991)).…”

Forecasting interest rates with shifting endpoints

“…Third, several recent papers use survey data in analysis of the term structure. Survey forecasts are used in the estimation of term structure models, or in testing model implications, by, among others, Piazzesi et al (2013), Chun (2011), Ehling et al (2013 and Kim and Orphanides (2012). Chun (2012) compares the individual survey forecasts themselves with models for the yield curve.…”

Robust Bond Risk Premia

“…Cieslak and Povala (2015) and Bauer and Rudebusch (2017) noted that in affine yield-curve models, even small measurement errors can make it impossible to recover z t from observed yields. Third, statistical expectations may differ from subjective expectations due to learning (as in, for example, Piazzesi et al, 2015). Fourth, there may be singularities, non-linearities, or structural breaks that prevent invertibility.…”