Journal
ENERGY ECONOMICS
Volume 97, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.eneco.2021.105205
Keywords
Bond yield; Oil prices predictability; Predictive regression; Out-of-sample forecasting; Asset allocation
Categories
Funding
- National Natural Science Foundation of China [71771030, 11301041]
- Fund of Hunan Provincial Education Department [19A007]
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The study shows that using long-term government bond yield, corporate bond yields spread, and Treasury bill rate can effectively predict WTI and Brent spot prices. These variables have substantial explanatory power on oil returns and there are significant Granger causality relationships between them. Additionally, the predictive abilities of bond yield variables can be significantly enhanced with multivariate prediction methods, partially due to their ability to capture oil market sentiment.
Using long-term government bond yield (LTY), corporate bond yields spread (DFY) and Treasury bill rate (TBL) as the proxies, we find bond yield can effectively predict WTI and Brent spot prices. In-sample analysis indicates that bond yield variables have substantial explanatory power on oil returns, and there are significant Granger causality relationships from LTY and DFY to oil returns. In out-of-sample forecast, bond yield variables defeat historical average benchmark as well as the competing predictors from both statistical and economic perspectives. Moreover, the predictive abilities of bond yield variables can be tremendously enhanced with multivariate prediction methods. We prove that the prediction power of bond yield variables partially stems from their abilities on capturing oil market sentiment. Our findings survive a series of robustness checks. (c) 2021 Elsevier B.V. All rights reserved.
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