scholarly journals Does Crude Oil Market Efficiency Improve After the Lift of the U.S. Export Ban? Evidence From Time-Varying Hurst Exponent

2020 ◽  
Vol 8 ◽  
Author(s):  
Yinghui Shao
Author(s):  
Louis H. Ederington ◽  
Chitru S. Fernando ◽  
Kateryna V. Holland ◽  
Thomas K. Lee

Author(s):  
S. A. Zolina ◽  
I. A. Kopytin ◽  
O. B. Reznikova

In 2018 the United States surpassed Saudi Arabia and Russia to become the largest world oil producer. The article focuses on the mechanisms through which the American shale revolution increasingly impacts functioning of the world oil market. The authors show that this impact is translated to the world oil market mainly through the trade and price channels. Lifting the ban on crude oil exports in December 2015 allowed the United States to increase rapidly supply of crude oil to the world oil market, the country’s share in the world crude oil exports reached 4,4% in 2018 and continues to rise. The U.S. share in the world petroleum products exports, on which the American oil sector places the main stake, reached 18%. In parallel with increasing oil production the U.S. considerably shrank crude oil import that forced many oil exporters to reorient to other markets. Due to high elasticity of tight oil production to the oil price increases oil from the U.S. has started to constrain the world oil price from above. According to the majority of authoritative forecasts, oil production in the U.S. will continue to increase at least until 2025. Since 2017 the tendency to the increasing expansion of supermajors into American unconventional oil sector has become noticeable, what will contribute to further strengthening of the U.S. position in the world oil market and accelerate its restructuring.  


Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1403
Author(s):  
Lu-Tao Zhao ◽  
Shun-Gang Wang ◽  
Zhi-Gang Zhang

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.


2012 ◽  
Vol 129 ◽  
pp. 119-137 ◽  
Author(s):  
Walid Mensi ◽  
Chaker Aloui ◽  
Manel Hamdi ◽  
Duc Khuong Nguyen

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Majid Mirzaee Ghazani ◽  
Mohammad Ali Jafari

AbstractThis study examined the evolving oil market efficiency by applying daily historical data to the three benchmark cryptocurrencies (Bitcoin, Ethereum, and Ripple), gold, and West Texas Intermediate (WTI) crude oil. The data coverage of daily returns was from August 2015 to April 2019. We applied two alternative tests to examine linear and nonlinear dependency, i.e., automatic portmanteau and generalized spectral tests. The analysis of observed results validated the adaptive market hypothesis (AMH) in all markets, but the degree of adaptability between the data was different. In this study, we also analyzed the existence of evolutionary behavior in the market. To achieve this goal, we checked the results by applying the rolling-window method with three different window lengths (50, 100, and 150 days) on the test statistics, which was consistent with the findings of AMH.


Sign in / Sign up

Export Citation Format

Share Document