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Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach

Lu-Tao Zhao (), Li-Na Liu (), Zi-Jie Wang () and Ling-Yun He
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Lu-Tao Zhao: School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
Li-Na Liu: School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
Zi-Jie Wang: School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China

Sustainability, 2019, vol. 11, issue 14, 1-20

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.

Keywords: oil price volatility; risk identification; VaR; big data; natural language processing; two-layer non-negative matrix factorization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2019
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