Unveiling the Effect of Mean and Volatility Spillover between the United States Economic Policy Uncertainty and WTI Crude Oil Price

Su, Ruixin; Du, Jianguo; Shahzad, Fakhar; Long, Xingle

doi:10.3390/su12166662

Cited by 12 publications

(6 citation statements)

References 47 publications

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“…The research results presented in this paper also contribute to the work of policymakers [37] because the WT-FNN model results indicated that recent data contained more useful information than historic data for crude oil price prediction. Therefore, policy makers should not focus on similar events in history when forecasting crude oil prices but should pay more attention to recent events and assess their influence on crude oil prices to predict crude oil prices more accurately.…”

Section: Discussion Of the Results And Conclusionmentioning

confidence: 66%

“…In this paper, we used the previous 4 days' crude oil prices to predict crude oil prices on the 5th day; therefore, the number of input layer neurons was four. According to [36,37], when the number of hidden layer neurons is half the number of input layer neurons, the predictive effect is best. Therefore, we determined that the number of input layer neurons was four, the hidden layer was two, and the output layer was one; that is, crude oil prices on the fifth day were predicted by the crude oil prices of the previous 4 days.…”

Section: Different Neuronsmentioning

confidence: 99%

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Forecasting Crude Oil Prices with a WT-FNN Model

Wang

Fang

2022

Energies

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In order to improve the accuracy of forecasting crude oil prices, a new crude oil price forecasting method is introduced in the paper that is a combination of the FNN model and the stochastic time effective function—namely, the WT-FNN model. The FNN model keeps track of the historical values of crude oil prices and predicts future crude oil prices, and the stochastic time effective function gives greater weight to recent information and smaller weight to old information, thus making the prediction of crude oil prices more reasonable. We selected the daily data of Brent crude oil prices from 4 January 2000 to 30 September 2021 as research objects and then used the WT-FNN model to train and predict the research objects. By comparing it to the benchmark model, we found that the predictive effect of the WT-FNN model was better than the FNN model and the no-change model. The results also passed a robustness test.

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Section: Discussion Of the Results And Conclusionmentioning

confidence: 66%

Section: Different Neuronsmentioning

confidence: 99%

Forecasting Crude Oil Prices with a WT-FNN Model

Wang

Fang

2022

Energies

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“…Energy prices (Karstensen and Peters 2018), gross domestic product (GDP) (Liu et al 2023) and total carbon emissions (Green 2021) play an important role in predicting the CEA prices. The West Texas Intermediate (WTI) crude oil has become the worldwide standard for crude oil pricing due to the global military as well as economic power of the United States (Su et al 2020). Eviews (Econometric Views) software is one of the most popular econometric software for the rapid creation, estimation, testing and application of econometric models of the quantitative laws of socioeconomic relations and economic activities (Xie 2023).…”

Section: Carbon Emissions Allowances (Ceas) Prices Projectionmentioning

confidence: 99%

Projection of future carbon benefits by photovoltaic power potential in China using CMIP6 statistical downscaling data

Niu,

Wu,

Qin

et al. 2023

Environ. Res. Lett.

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Solar photovoltaic (PV) systems is an affordable solution that significantly contribute to climate adaptation and resilience, energy security and greenhouse gas (GHG) abatement with respect to fossil fuel electricity generation. Currently, available studies on the benefits of photovoltaic power generation only consider the electricity consumption and do not account for the possible future benefits from carbon trading under the combined impacts of pollution emissions and socio-economic. In this study, the downscaling and bias correction were applied to the Coupled Model Inter-comparison Project Phase 6 (CMIP6) multi-model mean data based on the historical data from the China Meteorological Administration (CMA) stations. The corrected measurements of meteorology were used to explore the PV power potential and the environmental and economic benefits offset by solar power generation under SSP126, SSP245 and SSP585 in China during 2023-2100. We found that the annual mean PV power potential across mainland China ranged from 1-37 Wm-2 and demonstrated a decreasing trend in the Northwest China and an increasing trend in the Southeast China. Compared to thermal power generation, electricity from solar energy will counteract the total emissions of annual mean 139.54×105 t CO2, 1702 t SO2, 2562 t NOX and 3710 t dust in China in SSP126 scenario. The results of variable importance assessment showed that the coal price (44.46%), WTI crude oil price (42.17 %), natural gas price (6.17 %) and GDP (3.17 %) contribute the most to the carbon emissions allowances (CEA) price. Against a “carbon peak” background in China, the CEA price will reach 80 CNY/t CO2 by 2030 in China, with the carbon trading value potential ranging from 20 billion to 200 billion CNY of each pixel (10 km×10 km) by 2030. This study would have important implications for the environmental construction and future investment and construction of PV systems in China.

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“…Previous research has established that a variety of geopolitical risk events, such as macroeconomic announcements [15,35,36], significant political occurrences [20,37], and economic policy uncertainties [14,38], affect asset price returns. Geopolitical risk, as defined by [39], refers to the "efforts of states and organizations to assert control and vie for territory...the risks arising from wars, acts of terrorism, and tensions between states that disrupt the usual and peaceful progression of international relations".…”

Section: Spillover Of Geopolitical Events On Oil Stock Returnsmentioning

confidence: 99%

Spillovers across the Asian OPEC+ Financial Market

Vuković,

Dekpo-Adza,

Khmelnitskiy

et al. 2023

Mathematics

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This research utilizes the Diebold and Yilmaz spillover model to examine the correlation between geopolitical events, natural disasters, and oil stock returns in Asian OPEC+ member countries. The study extends prior research by investigating the dynamics of the Asian OPEC+ oil market in light of recent exogenous events. The analysis commences by creating a self-generated Asian OPEC+ index, which demonstrates significant volatility, as indicated by GARCH (1, 1) model estimation. The results obtained from the Diebold and Yilmaz spillover test indicate that, on average, there is a moderate degree of connectedness among the variables. However, in the event of global-level shocks or shocks specifically affecting Asian OPEC+ countries, a heightened level of connectedness is found. Prominent instances of spillover events observed in the volatility analysis conducted during the previous decade include the COVID-19 pandemic, the conflict between Russia and Ukraine, and the Turkey earthquake of 2023. Based on the facts, it is recommended that investors take into account the potential risks linked to regions that are susceptible to natural calamities and geopolitical occurrences while devising their portfolios for oil stocks. The results further highlight the significance of integrating these aspects into investors’ decision-making procedures and stress the need for risk management tactics that consider geopolitical risks and natural disasters in the oil equity market.

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Unveiling the Effect of Mean and Volatility Spillover between the United States Economic Policy Uncertainty and WTI Crude Oil Price

Cited by 12 publications

References 47 publications

Forecasting Crude Oil Prices with a WT-FNN Model

Forecasting Crude Oil Prices with a WT-FNN Model

Projection of future carbon benefits by photovoltaic power potential in China using CMIP6 statistical downscaling data

Spillovers across the Asian OPEC+ Financial Market

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