Bond Risk Premia and Gaussian Term Structure Models
Cochrane and Piazzesi (2005) show that (i) lagged forward rates improve the predictability of annual bond returns, adding to current forward rates, and that (ii) a Markovian model for monthly forward rates cannot generate the pattern of predictability in annual returns. These results stand as a challenge to modern Markovian dynamic term structure models (DTSMs). We develop the family of conditional mean DTSMs where the yield dynamics depend on current yields and their history. Empirically, we find that (i) current and past yields generate cyclical risk-premium variations, (ii) the model risk premia offer better returns forecasts, and (iii) the model coefficients are close to Cochrane-Piazzesi regressions of long-horizon returns. Yield decompositions differ significantly from what a standard model suggests - the expectation component decreases less in a recession and increases less in the recovery. A small Markovian factor “hidden” in measurement error (Duffee, 2011) explains some of the differences but is not sufficient to match the evidence.