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: View citations in EconPapers (64)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:72:y:2018:i:c:p:320-332

DOI: 10.1016/j.econmod.2018.02.009

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