Forecasting crude oil risk: A multiscale bidirectional generative adversarial network based approach. (February 2023)

• Record Type: Journal Article
• Authors: Zou, Yingchao; Yu, Lean; He, Kaijian
• Is Part Of: Expert systems with applications. Volume 212(2023)
• Publication Date: 2023-02
• Subjects: Crude oil risk -- Value-at-risk -- Multivariate empirical mode decomposition (MEMD) model -- Multi-scale analysis -- Bidirectional generative adversarial network model; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118743
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: The crude oil market is known to be subject to the influence of transient and extreme events. The rare and infrequent nature of these events leads to problems such as a lack of data for the estimation of reliable risk measures in the crude oil market. In this paper, an innovative MEMD-BiGAN risk forecasting methodology combining the power of multi scale analysis and Generative Adversarial Network has been proposed. BiGAN has been introduced as an innovative method from the machine learning field to produce the augmented dataset with sufficient number of observations. Then Historical Simulation method can be employed to estimate market risk level in the multiscale domain, where the final risk forecasts taking into account the transient risk factors are more accurate and reliable. MEMD-BiGAN has been applied to model the portfolio of daily trading data in the major crude oil markets including West Texas Intermediate, Brent and OPEC markets. Results suggest that the MEMD-BiGAN model can achieve improved risk coverage. This implies that the incorporation of the transient risk factors using the BiGAN model is essential to more accurate modeling of the risk measures in the turbulent market environment. Highlights: The multiscale risk structure is decomposed using MEMD model. The mixture of HS model is used to model the risk structure. The transient risk factor is identified and modeled. BiGAN is introduced to extend and augment the limited transient risk data. Improvement in risk forecasting accuracy has been achieved.