Abstract
The rapid fluctuations in global crude oil prices are one of the important factors affecting both the sustainable development and the green transformation of the global economy. To accurately measure the risks of crude oil prices, in the context of big data, this study introduces the two-layer non-negative matrix factorization model, a kind of natural language processing, to extract the dynamic risk factors from online news and assign them as weighted factors to historical data. Finally, this study proposes a giant information history simulation (GIHS) method which is used to forecast the value-at-risk (VaR) of crude oil. In conclusion, this paper shows that considering the impact of dynamic risk factors from online news on the VaR can improve the accuracy of crude oil VaR measurement, providing an effective tool for analyzing crude oil price risks in oil market, providing risk management support for international oil market investors, and providing the country with a sense of risk analysis to achieve sustainable and green transformation.
Highlights
As a strategic resource, crude oil is the foundation of global economic development and global commodity markets [1,2]
We modeled the Brent oil returns from October 2001 to December 2011 to forecast the out-of-sample VaR from January 2012 to October 2018
The HSAF method is based on the autoregressive moving average model (ARMA) model, its VaR is the sum of forecasting value with ARMA, and the quantile of the corresponding error sequence
Summary
Crude oil is the foundation of global economic development and global commodity markets [1,2]. Slight fluctuations in crude oil prices stimulate the development of the world economy. Abnormal fluctuations in crude oil prices, unleash clear signals for the economy to pinpoint and solve the problems as soon as possible. Oil prices are closely related to the sustainable development of the world economy. Emergent events and political and economic events, (e.g., oil workers’ strike action, financial crises, and two Gulf Wars) have severely affected the supply–demand balance of crude oil markets, which has resulted in more complex, rapidly changing crude oil risks [6]. The fluctuations of global oil prices have caused global concerns: how to improve the accuracy of VaR forecasting and how to conduct risk management have become the focus of scholars [7]
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