Bond Risk Premia and Realized Jump Risk

Abstract

We find that augmenting a regression of excess bond returns on the term structure of forward rates with a rolling estimate of the mean realized jump size - identified from high-frequency bond returns using the bi-power variation technique - substantially increases the R2 of the regression. This result is consistent with the setting of an unspanned risk factor in which the conditional distribution of excess bond returns is affected by a state variable that does not lie in the span of the term structure of yields or forward rates. The return predictability from augmenting the regression of excess bond returns on forward rates with the jump mean easily dominates the return predictability offered by instead augmenting the regression with options-implied volatility or realized volatility from high frequency data. The significant enhancement of bond return predictability is robust to different forecasting horizons, to using nonoverlapping returns and to the choice of different window sizes in computing the jump risk measures. In an out-of-sample forecasting exercise, inclusion of the jump mean reduces the root mean square prediction error by about 40 percent.

Keywords: Unspanned Stochastic Volatility, Expected Excess Bond Returns, Expectations Hypothesis, Countercyclical Risk Premia, Realized Jump Risk, Bi-Power Variation

JEL Classification: G12, G14, E43, C22