Crude oil risk forecasting : new evidence from multiscale analysis approach

He, K; Tso, GKF; Zou, Y; Liu, JL

Abstract

Fluctuations in the crude oil price allied to risk have increased significantly over the last decade frequently varying at different risk levels. Although existing models partially predict such variations, so far, they have been unable to predict oil prices accurately in this highly volatile market. The development of an effective, predictive model has therefore become a prime objective of research in this field. Our approach, albeit based in part on previous research, develops an original methodology, in that we have created a risk forecasting model with the ability to predict oil price fluctuations caused by changes in both fundamental and transient risk factors. We achieve this by disintegrating the multi-scale risk-structure of the crude oil market using Variational Mode Decomposition. Normal and transient risk factors are then extracted from the crude oil price using Variational Mode Decomposition and modelled separately using the Quantile Regression Neural Network (QRNN) model. Both risk factors are integrated and ensembled to produce the risk estimates. We then apply our proposed risk forecasting model to predicting future downside risk level in three major crude oil markets, namely the West Taxes Intermediate (WTI), the Brent Market, and the OPEC market. The results demonstrate that our model has the ability to capture downside risk estimates with significantly improved precision, thus reducing estimation errors and increasing forecasting reliability.

Citation

He, K., Tso, G., Zou, Y., & Liu, J. (2018). Crude oil risk forecasting : new evidence from multiscale analysis approach. Energy Economics, 76, 574-583. https://doi.org/10.1016/j.eneco.2018.10.001

• Journal Article Type: Article
• Acceptance Date: Oct 8, 2018
• Online Publication Date: Oct 15, 2018
• Publication Date: Oct 1, 2018
• Deposit Date: Nov 19, 2018
• Publicly Available Date: Apr 15, 2020
• Journal: Energy Economics
• Print ISSN: 0140-9883
• Publisher: Elsevier
• Volume: 76
• Pages: 574-583
• DOI: https://doi.org/10.1016/j.eneco.2018.10.001
• Publisher URL: https://doi.org/10.1016/j.eneco.2018.10.001
• Related Public URLs: https://www.journals.elsevier.com/energy-economics
• Additional Information: Funders : The National Natural Science Foundation of China;Humanities and Social Science Youth Foundation of Ministry of Education of China