Crude oil risk forecasting: new evidence from multiscale analysis approach

Kaijian He, Geoffrey K.F. Tso, Yingchao Zou, Jia Liu

    Research output: Contribution to journalArticlepeer-review

    136 Downloads (Pure)

    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.
    Original languageEnglish
    Pages (from-to)574-583
    JournalEnergy Economics
    Volume76
    Early online date15 Oct 2018
    DOIs
    Publication statusPublished - 15 Oct 2018

    Keywords

    • Crude oil risk forecasting
    • Variational Mode Decomposition
    • Value at Risk
    • Normal Risk
    • Transient Risk
    • Multiscale analysis
    • Quantile Regression Neural Network model

    Fingerprint

    Dive into the research topics of 'Crude oil risk forecasting: new evidence from multiscale analysis approach'. Together they form a unique fingerprint.

    Cite this