Forecasting Oil Commodity Spot Price in a Data-Rich Environment

Sabri Boubaker, Z. Liu and Y. Zhang

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Abstract: Statistical properties that vary with time represent a challenge for time series forecasting. This paper proposes a change point-adaptive-RNN (CP-ADARNN) framework to predict crude oil prices with high-dimensional monthly variables. We first detect the structural breaks in predictors using the change point technique, and subsequently train a prediction model based on ADARNN. Using 310 economic series as exogenous factors from 1993 to 2021 to predict the monthly return on the WTI crude oil real price, CP-ADARNN outperforms competing benchmarks by 12.5% in terms of the root mean square error and achieves a correlation of 0.706 between predicted and actual returns. Furthermore, the superiority of CP-ADARNN is robust for Brent oil price as well as during the COVID-19 pandemic. The findings of this paper provide new insights for investors and researchers in the oil market. \textcopyright 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Keywords: Change point detection; COVID-19; Oil price prediction; Recursive neural network (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

Published in Annals of Operations Research, 2022, ⟨10.1007/s10479-022-05004-8⟩

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Working Paper: Forecasting Oil Commodity Spot Price in a Data-Rich Environment (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04444805

DOI: 10.1007/s10479-022-05004-8

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