Forecasting Oil Commodity Spot Price in a Data-Rich Environment - Normandie Université
Article Dans Une Revue Annals of Operations Research Année : 2022

Forecasting Oil Commodity Spot Price in a Data-Rich Environment

Z. Liu
  • Fonction : Auteur
Y. Zhang
  • Fonction : Auteur

Résumé

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.

Dates et versions

hal-04444805 , version 1 (07-02-2024)

Identifiants

Citer

S. Boubaker, Z. Liu, Y. Zhang. Forecasting Oil Commodity Spot Price in a Data-Rich Environment. Annals of Operations Research, 2022, ⟨10.1007/s10479-022-05004-8⟩. ⟨hal-04444805⟩
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