Bond Risk Premia and Gaussian Term Structure Models

Existing results show that (i) lagged forward rates help predict bond returns and (ii) modern Markovian dynamic term structure models (DTSMs) cannot match the evidence [Cochrane JH, Piazzesi M (2005) Bond risk premia. Amer. Econom. Rev. 95(1):138–160]. We develop the family of conditional mean DTSMs where the dynamics depend on current yields and their history through a moving-average component. Our preferred conditional mean model combines one moving average with the usual three Gaussian risk factors, closely matches the bond risk premium measured from predictive regressions, and provides better forecasts of bond returns. Our framework nests Duffee’s models with a small “hidden” factor [Duffee G (2011) Information in (and not in) the term structure. Rev. Financial Stud. 24(9):2895–2934], and our results compare favorably with his five-factor model. Conditional mean models are easier to estimate than state-space term structure models based on Kalman estimates of latent factors. The online appendix is available at https://doi.org/10.1287/mnsc.2016.2602 . This paper was accepted by Lauren Cohen, finance.

• 저널Management Science · 64(3) · 1413–1439
• 토픽Stochastic processes and financial applications · Finance
• DOI10.1287/mnsc.2016.2602
• 저자Bruno Feunou, Jean‐Sébastien Fontaine