EconPapers    
Economics at your fingertips  
 

Forecasting the aggregate oil price volatility in a data-rich environment

Feng Ma, Jing Liu, M.I.M. Wahab and Yaojie Zhang

Economic Modelling, 2018, vol. 72, issue C, 320-332

Abstract: This paper explores the effectiveness of a large set of indicators in forecasting crude oil price volatility, including uncertainty and market sentiment, macroeconomic indicators, and technical indicators. Using the OLS, LASSO regression, and various combination forecasts, we obtain several noteworthy findings. First, we determine which indicators most effectively forecast oil price volatility. Specifically, the uncertainty index is notable. Second, in general, combination strategies and LASSO produce statistically and economically significant forecasts. Third, the combined and LASSO strategies perform considerably better during recessions than expansions. Overall, our study provides which indicators and strategies can improve forecasting accuracy in the oil market.

Keywords: Volatility forecasting; Uncertainty and market sentiment; Macroeconomic variables; Technical indicators; Combinations forecasts (search for similar items in EconPapers)
JEL-codes: C22 C32 C53 F40 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0264999317315833
Full text for ScienceDirect subscribers only

Related works:
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:ecmode:v:72:y:2018:i:c:p:320-332

Access Statistics for this article

Economic Modelling is currently edited by S. Hall and P. Pauly

More articles in Economic Modelling from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

 
Page updated 2018-11-07
Handle: RePEc:eee:ecmode:v:72:y:2018:i:c:p:320-332