Forecasting the realized variance of oil-price returns using machine learning: Is there a role for U.S. state-level uncertainty?

Oguzhan Cepni, Rangan Gupta, Daniel Pienaar and Christian Pierdzioch

Energy Economics, 2022, vol. 114, issue C

Abstract: Predicting the variance of oil-price returns is of paramount importance for policymakers and investors. Recent research has focused on whether disaggregate measures of economic-policy uncertainty provide better forecasts. Given that the United States (U.S.) is a major player in the international oil market, we extend this line of research by exploring by means of machine-learning techniques whether accounting for U.S. state-level measures of economic-policy uncertainty results in more accurate forecasts. We find improvements in forecast accuracy, especially when we study intermediate and long forecast horizons. This finding is robust to various changes in the model configuration (realized variance vs. realized volatility, sample period, recursive vs. rolling-estimation window, loss function of forecast consumers). Understandably, our findings have important implications for oil traders and policy authorities.

Keywords: Oil price; Realized variance; Forecasting; Machine learning; Aggregate and regional uncertainties (search for similar items in EconPapers)
JEL-codes: C22 C53 D8 Q02 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0140988322003723
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Forecasting the Realized Variance of Oil-Price Returns Using Machine-Learning: Is there a Role for U.S. State-Level Uncertainty? (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:114:y:2022:i:c:s0140988322003723

DOI: 10.1016/j.eneco.2022.106229

Access Statistics for this article

Energy Economics is currently edited by R. S. J. Tol, Beng Ang, Lance Bachmeier, Perry Sadorsky, Ugur Soytas and J. P. Weyant

More articles in Energy Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().