  We explore the importance of information hidden from the yield curve and
assess how valuable the unspanned risks are to a real-time Bayesian investor
seeking to forecast excess bond returns and maximise her utility. We propose a
novel class of arbitrage-free unspanned Dynamic Term Structure Models (DTSM),
that embed a stochastic market price of risk specification. We develop a
suitable Sequential Monte Carlo (SMC) inferential and prediction scheme that
guarantees joint identification of parameters and latent states and takes into
account all relevant uncertainties. We find that latent factors contain
significant predictive power above and beyond the yield curve, providing
improvement to the out-of-sample predictive performance of models, especially
at shorter maturities. Most importantly, they are capable of exploiting
information hidden from the yield curve and translate the evident statistical
predictability into significant utility gains, out-of-sample. The hidden
component associated with slope risk is countercyclical and links with real
activity.
