This paper studies the predictability of bond risk premia by means of expectations to future business conditions using survey forecasts from the Survey of Professional Forecasters. We show that expected business conditions consistently affect excess bond returns and that the inclusion of expected business conditions in standard predictive regressions improve forecast performance relative to models using information derived from the current term structure or macroeconomic variables. The results are confirmed in a real-time out-of-sample exercise, where the predictive accuracy of the models is evaluated both statistically and from the perspective of a mean-variance investor that trades in the bond market.Keywords: Bond risk premia, expected business conditions, predictability, economic value, expectations hypothesis, time-varying risk premia JEL Classification: E43, E44, E47, G11, G12.
IntroductionThere is mounting evidence that the risk premia required by investors for holding Treasury bonds contain a time-varying and predictable component. One strand of research relates such variations to forward spreads (Fama and Bliss, 1987;Fama, 2006), yield spreads (Keim and Stambaugh, 1986;Campbell and Shiller, 1991), and forward rates (Cochrane and Piazzesi, 2005;Dahlquist and Hasseltoft, 2013;Zhu, 2015), whereas more recent studies link the predictable component to factors whose variations lie outside the span of current yields. Existing research within this strand has uncovered a wide array of factors including jump risk (Wright and Zhou, 2009), option prices (Almeida, Graveline, and Joslin, 2011), and macroeconomic variables (Ilmanen, 1995;Moench, 2008;Cooper and Priestley, 2009;Ludvigson and Ng, 2009;Wright, 2011;Favero, Niu, and Sala, 2012;Joslin, Priebsch, and Singleton, 2014;Zhou and Zhu, 2015).While existing studies mainly rely on information in the current term structure and business environment to explain variations in the predictable component of bond risk premia, our study takes a forward-looking perspective by studying the link between expected business conditions and bond risk premia using survey forecasts from the Survey of Professional Forecasters (SPF). Albeit empirical studies frequently conclude that macroeconomic fundamentals carry information about bond risk premia not already captured by the yield curve, they rarely account for issues with publication lags and data revisions in macroeconomic time series. As recently demonstrated by Ghysels, Horan, and Moench (2014), such features may drive a wedge between the factor spaces spanned by latent common factors extracted from a panel of revised and non-revised data, respectively, implying that a sizable fraction of the predictability documented by Ludvigson and Ng (2009) may largely be driven by revision and publication lag components that are unavailable to an investor in real-time. Survey forecasts, as a result, provide an ideal data source for studying the predictability of bond risk premia as they are model-free, available in real-time, and not...
