Bond Risk Premia and Gaussian Term Structure Models

• 成果类型: Article
• 署名作者: Feunou, Bruno; Fontaine, Jean-Sebastien
• 署名单位: Bank of Canada
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2016.2602
• 发表日期: 2018
• 页码: 1413-1439
• 关键词: term structure models; Bond risk premium; unspanned risk

摘要

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.