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Oil Price Forecasting Using a Time-Varying Approach

Lu-Tao Zhao, Shun-Gang Wang and Zhi-Gang Zhang
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Shun-Gang Wang: School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
Zhi-Gang Zhang: School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China

Energies, 2020, vol. 13, issue 6, 1-16

Abstract: The international crude oil market plays an important role in the global economy. This paper uses a variable time window and the polynomial decomposition method to define the trend term of time series and proposes a crude oil price forecasting method based on time-varying trend decomposition to describe the changes in trends over time and forecast crude oil prices. First, to characterize the time-varying characteristics of crude oil price trends, the basic concepts of post-position intervals, pre-position intervals and time-varying windows are defined. Second, a crude oil price series is decomposed with a time-varying window to determine the best fitting results. The parameter vector is used as a time-varying trend. Then, to quantitatively describe the continuation of the time-varying trend, the concept of the trend threshold is defined, and a corresponding algorithm for selecting the trend threshold is given. Finally, through the predicted trend thresholds, the historical reference data are selected, and the time-varying trend is combined to complete the crude oil price forecast. Through empirical research, it is found that the time-varying trend prediction model proposed in this paper achieves a better prediction than several common models. These results can provide suggestions and references for investors in the international crude oil market to understand the trends of oil prices and improve their investment decisions.

Keywords: Time-varying characteristic; Trend threshold; Time-varying trend decomposition model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (3)

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