IAEE Members and subscribers to The Energy Journal: Please log in to access the full text article or receive discounted pricing for this article.

Prepress Content: The following article is a preprint of a scientific paper that has completed the peer-review process and been accepted for publication within The Energy Journal.

While the International Association for Energy Economics (IAEE) makes every effort to ensure the veracity of the material and the accuracy of the data therein, IAEE is not responsible for the citing of this content until the article is actually printed in a final version of The Energy Journal. For example, preprinted articles are often moved from issue to issue affecting page numbers, and actual volume and issue numbers. Care should be given when citing Energy Journal preprint articles.

Oil price volatility is effective in predicting food price volatility. Or is it?

Abstract:
Volatility spillovers between food commodities and oil prices have been identified in the literature, yet, there has been no empirical evidence to suggest that oil price volatility improves real out-of-sample forecasts of food price volatility. In this study we provide new evidence showing that oil price volatility does not improve forecasts of agricultural price volatility. This finding is based on extensive and rigorous testing of five internationally traded agricultural commodities (soybeans, corn, sugar, rough rice and wheat) and two oil benchmarks (Brent and WTI). We employ monthly and daily oil and food price volatility data and two forecasting frameworks, namely, the HAR and MIDAS-HAR, for the period 2nd January 1990 until 31st March 2017. Results indicate that oil volatility-enhanced HAR or MIDAS-HAR models cannot systematically outperform the standard HAR model. Thus, contrary to what has been suggested by the existing literature based on in-sample analysis, we are unable to find any systematic evidence that oil price volatility improves out-of-sample forecasts of food price volatility. The results remain robust to the choice of different out-of-sample forecasting periods and three different volatility measures.

Download Executive Summary Purchase ( $25 )

Download Appendix 

Keywords: Forecasting, Food price volatility, Heterogeneous Autoregressive, Mixed-data sampling, Oil price volatility, Model Confidence Set

DOI: 10.5547/01956574.42.6.icha

Published in Volume 42, Number 6 of the bi-monthly journal of the IAEE's Energy Economics Education Foundation.

 

© 2021 International Association for Energy Economics | Privacy Policy | Return Policy