What drives natural gas prices in the United States? – A directed acyclic graph approach

Ji, Qiang; Zhang, Haiying; Geng, Jiang-Bo

doi:10.1016/j.eneco.2017.11.002

Cited by 79 publications

(24 citation statements)

References 37 publications

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“…Last but not least, if we study the case of 2 to 4 years' phase-difference we find that phase-differences always are between ] 2 / , 0 [  indicating that both series move cyclically and oil price leading natural gas price. So in general, the effect of natural gas price on WTI price, especially in the wake of the so-called shale gas revolution is not evident, and tends to support the works of Geng et al, (2016aGeng et al, ( , b, c, 2017, Ji et al, (2018), Zhang and Ji (2018). 4…”

Section: Figurementioning

confidence: 97%

“…However, in terms of the question we ask dealing with effect of natural gas on oil prices in the wake of the shale gas revolution, our paper is most closely related to the works of Geng et al, (2016aGeng et al, ( , b, c, 2017, Ji et al, (2018), Zhang and Ji (2018). These studies using variety of econometric methods covering nonparametric, regime-switching, directed acyclic graphs, multi-scale perspective, in general tends to suggest weak relationship between the two prices of concern.…”

Section: Introductionmentioning

confidence: 99%

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A wavelet analysis of the relationship between oil and natural gas prices

et al. 2019

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In this paper, we aim to explore the relationship between natural gas and crude oil prices for the U.S. economy over the time period 1997 and 2017 in both the unconditional and conditional framework by conditioning the relationship on natural gas production. The time period covers the recent shale gas supply boom. Our results indicate that during the shale gas revolution period of 2007-2013, oil and natural gas prices were cyclical and oil prices were leading natural gas prices. Once we control for the natural gas production we find that significant or high wavelet coherency is observed during 2000-2015 for 3 to 4 years scale. These results have implications for cross market policy effects.

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Section: Figurementioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

A wavelet analysis of the relationship between oil and natural gas prices

et al. 2019

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“…The consumption of natural gas is influenced by the environment, government policies, and its price (Fang et al, 2018b;. Existing research indicates that its consumption and price, along with economic growth (mainly measured in terms of GDP) form a complex system consisting of many elements (Ji and Zhang, 2018;Cheng et al, 2019), and that the relationships among these elements are nonlinear and complex in nature (Fang et al, 2012(Fang et al, , 2017(Fang et al, , 2018bWang et al, 2015). Exploring these relationships can assist the government of Fuyang City in its quest to reduce the pollution in and around the city.…”

Section: Establishment Of Urban Natural Gas Energy Modelmentioning

confidence: 99%

Strategic Research on the Urban Natural Gas Energy System Under the Path to Ecological Civilization: Fuyang City Case Study

Hao

Xia

2020

Front. Energy Res.

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To help meet the strategic development needs of urban energy systems today and reduce carbon emissions, natural gas can play a pivotal transition role, especially in a country such as China, which has relied very heavily on coal for decades. Though making significant progress toward the maximum possible use of clean, renewable forms of energy, such as hydro, thermal, solar, and wind power, it will take a long time before China can completely abandon the use of fossil fuels. In this paper, using Fuyang City in Anhui Province as a case study, a four-dimensional differential equation model, based on the consumption, price, economic growth, and "ecological civilization construction" of natural gas energy, is developed, which incorporates concepts and calculations of natural gas energy intensity, "natural gas ecological civilization intensity," and "economic ecological civilization intensity." It is the first attempt ever made to quantify the construction of urban ecological civilization and discuss the evolutionary relationship among the internal variables of the system. Regression analysis, data fitting, neural network, and other methods are used to confirm the parameters of the system model. Through Matlab simulation of each variable and using an evolution map, a quantitative understanding of the role of natural gas is generated. The research findings reveal that paying attention to the environmental aspects of energy consumption is beneficial to economic growth. The paper concludes that, at present, the best ways for China to reduce its carbon emissions are to implement a market price and peak-valley prices for natural gas, improve the tiered price mechanism, appropriately reduce the economic growth rate, continue to adjust the industrial structure away from heavy industry, and scientifically manage the natural gas energy system. These reforms are of great practical significance in working toward a sustainable development path for the city.

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“…Secondly, the natural gas can be seen as an alternative energy resource to heating oil in US industry and power plants, thus, their demands can be shifted between each other based on the market prices [40]. Thirdly, the prosperity level of the natural gas market can be reflected based on the gas drilling activities [41]. Fourthly, the price of natural gas often suffers from the temperature and weather changes are also a key factor [42].…”

Section: Data Preparation and Descriptionmentioning

confidence: 99%

Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods

Zhang

Zhu

et al. 2019

Energies

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Natural gas has been proposed as a solution to increase the security of energy supply and reduce environmental pollution around the world. Being able to forecast natural gas price benefits various stakeholders and has become a very valuable tool for all market participants in competitive natural gas markets. Machine learning algorithms have gradually become popular tools for natural gas price forecasting. In this paper, we investigate data-driven predictive models for natural gas price forecasting based on common machine learning tools, i.e., artificial neural networks (ANN), support vector machines (SVM), gradient boosting machines (GBM), and Gaussian process regression (GPR). We harness the method of cross-validation for model training and monthly Henry Hub natural gas spot price data from January 2001 to October 2018 for evaluation. Results show that these four machine learning methods have different performance in predicting natural gas prices. However, overall ANN reveals better prediction performance compared with SVM, GBM, and GPR.Energies 2019, 12, 1680 2 of 17 usage of resources based on accurate predictions. Accurate natural gas price forecasting not only provides an important guide for effective implementation of energy policy and planning, but also is extremely significant in economic planning, energy investment, and environmental conservation. Therefore, researchers continue to study natural gas price forecasting models with great interest, with the aim of making predictions as accurate as possible in future.There are plenty of methods for analyzing and forecasting natural gas prices and machine learning is increasingly used. Machine learning algorithms can learn from historical relationships and trends in the data and make data-driven predictions or decisions. A great number of researchers have investigated natural gas price prediction with the aid of various machine learning methods so far. For instance, Abrishami and Varahrami mixed the data handling neural network technique with a rule-based expert system in forecasting natural gas prices [3]. Busse et al. used a nonlinear autoregressive exogenous model neural network [4]. Azadeh et al. studied a hybrid neuro-fuzzy method composed of ANN, fuzzy linear regression, and conventional regression [5]. Salehnia et al. developed several nonlinear models using the gamma test, including local linear regression, dynamic local linear regression, and ANN models [6]. Ceperic et al. proposed a strategic seasonality-adjusted, support vector regression machine-based model [7]. Su et al. utilized a least squares regression boosting algorithm in natural gas price prediction [8].As indicated in the abovementioned existing studies that exploited machine learning tools for natural gas price prediction, ANN and SVM are widely used machine learning methods in forecasting natural gas prices. In addition to ANN and SVM, this study will introduce two other common machine learning approaches, GBM and GPR (these two methods were used for forecasting hourly loads in US [9]). ...

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What drives natural gas prices in the United States? – A directed acyclic graph approach

Cited by 79 publications

References 37 publications

A wavelet analysis of the relationship between oil and natural gas prices

A wavelet analysis of the relationship between oil and natural gas prices

Strategic Research on the Urban Natural Gas Energy System Under the Path to Ecological Civilization: Fuyang City Case Study

Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods

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