Meeting Details

Title: Determinants of Bond Risk Premia Probability and Financial Mathematics Seminar Jingzhi (Jay) Huang, Finance Department, Smeal College of Business, PSU In this paper, we provide new and robust evidence on the power of macro variables for forecasting bond risk premia by using a recently developed model selection method--the supervised adaptive group least absolute shrinkage and selection operator" (lasso) approach. We identify a single macro factor that can not only subsume the macro factors documented in the existing literature but also can substantially raise their forecasting power for future bond excess returns. Specifically, we find that the new macro factor, a linear combination of four group factors (including employment, housing, and price indices), can explain the variation in excess returns on bonds with maturities ranging from two to five years up to 43%. The new factor is countercyclical and furthermore picks up unspanned predictability in bond excess returns. Namely, the new macro factor contains substantial information on expected excess returns (as well as expected future short rates) but has negligible impact on the cross section of bond yields.