Forecasting oil prices: New approaches

Rennan Kertlly de Medeiros, Cássio da Nóbrega Besarria, Diego Pitta de Jesus and Vinicius Phillipe de Albuquerquemello

Energy, 2022, vol. 238, issue PC

Abstract: This paper proposes alternative methodologies for oil price forecasting using mixed-frequency data and a textual sentiment indicator. The latter variable was extracted from oil market reports issued by the Energy Information Administration. We used the root mean square error (RMSE) to evaluate the forecasting accuracy of the econometric models. Compared with other econometric models, the mixed data sampling (MIDAS) model with high-frequency financial indicators and the sentiment index as explanatory variables performs better for forecasting crude oil prices.

Keywords: Forecasting oil prices; Time series; Econometrics; MIDAS model; Sentiment index (search for similar items in EconPapers)
JEL-codes: C53 C58 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 (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:238:y:2022:i:pc:s0360544221022167

DOI: 10.1016/j.energy.2021.121968

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