Natural Gas Spot Prices Research Articles

Estimation of global natural gas spot prices using big data and symbolic regression

This article provides an estimation of future natural gas spot prices on the global international market based on symbolic regression where the sensitivity analysis is performed to identify the most important input parameters. Numerical data sets, comprising various parameters, some of which demonstrate stronger correlations with the global spot price of natural gas, are utilised in this context. PySR (Python Symbolic Regression), a free and open-source software for symbolic regression written in Python, and Julia is used for the presented analysis. Based on the accuracy of the prediction and after sensitivity analysis performed in SALib software, some of the parameters are discovered to be more influencing on natural gas prices compared with others, making this approach suitable for further deeper energy analysis. The analysis shows that in general, global prices of natural gas are influenced mostly by the price of crude oil. The article also presents an overview of methods for predicting natural gas prices with a complementary contribution (interpretable models provided by symbolic regression and sensitivity analysis) tested on the real gas price time-series dataset.

Continuous weekly monitoring of methane emissions from the Permian Basin by inversion of TROPOMI satellite observations

Abstract. We quantify weekly methane emissions at 0.25∘ × 0.3125∘ (≈25 × 25 km2) resolution from the Permian Basin, the largest oil production basin in the US, by inverse analysis of satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI) from May 2018 to October 2020. The mean oil and gas emission from the region (± standard deviation of weekly estimates) was 3.7 ± 0.9 Tg a−1, higher than previous TROPOMI inversion estimates that may have used biased prior emissions or background assumptions. We find strong week-to-week variability in emissions superimposed on longer-term trends, and these are consistent with independent inferences of temporal emission variability from tower, aircraft, and multispectral satellite data. New well development and natural gas spot price were significant drivers of variability in emissions over our study period but the concurrent 50 % increase in oil and gas production was not. The methane intensity (methane emitted per unit of methane gas produced) averaged 4.6 % ± 1.3 % and steadily decreased from 5 %–6 % in 2018 to 3 %–4 % in 2020. While the decreasing trend suggests improvement in operator practices during the study period, methane emissions from the Permian Basin remained high, with methane intensity an order of magnitude above the industry target of <0.2 %. Our success in using TROPOMI satellite observations for weekly estimates of emissions from a major oil production basin shows promise for application to near-real-time monitoring in support of climate change mitigation efforts.

Natural gas spot price prediction research under the background of Russia-Ukraine conflict - based on FS-GA-SVR hybrid model

The ongoing Russia-Ukraine conflict has led to significant upheaval in the worldwide natural gas sector. Accurate natural gas price forecasting, as an essential tool for mitigating market uncertainty, plays a crucial role in commodity trading and regulatory decision-making. This study aims to develop a hybrid forecasting model, the FS-GA-SVR model, which integrates feature selection (FS), genetic algorithm (GA), and support vector regression (SVR) to investigate Henry Hub natural gas price prediction amidst the Russia-Ukraine conflict. The results show that: (1) The feature selection automates model input variable selection, decreasing the time required while improving the model's accuracy. (2) The use of genetic algorithm for selecting support vector regression hyperparameters significantly improves the accuracy of natural gas price predictions. The algorithm leads to a decrease of approximately 70% in measurement indicators. (3) During the Russia-Ukraine conflict, the FS-GA-SVR hybrid model demonstrates more consistent and accurate predictions for natural gas spot prices than the base SVR model. This study serves as a valuable theoretical reference for energy policymakers and natural gas market investors worldwide, supporting their ability to anticipate fluctuations in natural gas prices.

Electricity price spike formation and LNG prices effect under gross bidding scheme in JEPX

This study empirically analyzes the price spikes that occurred in the Japan Electric Power Exchange (JEPX) market in January 2021 and draws energy policy implications from the results. The contributions of this research are as follows. First, we propose a novel structural model to capture electricity price spikes based on the buy and sell bid curves of the JEPX order book and incorporate the effect of LNG spot price fluctuations on the price of electricity. Second, the results of our empirical analysis show that the model can adequately capture the price spike in the JEPX market that occurred in January 2021. Third, the estimation of the model parameters shows that the natural gas spot price immediately shifts the price curve upward. As an important energy policy implication from the results, we propose that to manage the risk of electricity price spikes, the LNG effect on the market players’ bidding behavior, rather than the marginal cost of the supply curve, be considered; from this perspective, the LNG spot price, rather than the trend of the LNG price a few months earlier, must be monitored, and the institutional design of the JEPX market should take this into account.

A Novel Model for Spot Price Forecast of Natural Gas Based on Temporal Convolutional Network

Natural gas is often said to be the most environmentally friendly fossil fuel. Its usage has increased significantly in recent years. Meanwhile, accurate forecasting of natural gas spot prices has become critical to energy management, economic growth, and environmental protection. This work offers a novel model based on the temporal convolutional network (TCN) and dynamic learning rate for predicting natural gas spot prices over the following two weekdays. The residual block structure of TCN provides good prediction accuracy, and the dilated causal convolutions minimize the amount of computation. The dynamic learning rate setting was adopted to enhance the model’s prediction accuracy and robustness. Compared with three existing models, i.e., the one-dimensional convolutional neural network (1D-CNN), gate recurrent unit (GRU), and long short-term memory (LSTM), the proposed model can achieve better performance over other models with mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean squared error (RMSE) scores of 4.965%, 0.216, and 0.687, respectively. These attractive advantages make the proposed model a promising candidate for long-term stability in natural gas spot price forecasting.

Multidimensional Risk-Based Real Options Valuation for Low-Carbon Cogeneration Pathways

Energy price fluctuations pose a significant risk and uncertainty to financial investments for new developments in conventional power and freshwater cogeneration facilities. This study attempts to address the problem of making robust valuation for low-carbon energy project investments subject to multi-dimensional price risk, particularly looking at some key research questions: (a) how does the correlation structure, or independence, between the price risks affect the project value; and (b) does adding flexibility in investment enhance or worsen the project valuation, given (a). This study identified three price factors with significant fluctuations that impact conventional power generation, namely: wholesale electricity spot price, natural gas spot price, and CO2 market price. The price factors were used to construct a multidimensional risk model and evaluate investment decisions for cogeneration project expansion in the future based on a low-carbon energy mix. To this end, five cogeneration configurations using combined-cycle gas turbine (CCGT) integrated with solar photovoltaics (PV) and carbon capture and storage (CCS) technologies were assessed. A combined price risk was initially estimated by transforming the given price factors representing maximum covariance using principal component analysis (PCA). The trend and volatilities in the major principal component scores (the combined price risk indicator) were modelled using the geometric Brownian motion stochastic process, whose parameters were determined and then used to perform time-series simulation and generate multiple realisations of the principal component. A back transformation was then applied to obtain the simulated values representing future uncertainties in the price factors. The effect of price risk and uncertainties were subsequently evaluated using a recombining binomial lattice model for real options analysis (ROA). There were financial gains when PV was mixed with conventional natural gas-fired technology. Investment in cogeneration configurations with (a) 25% PV share provided a 53% gain in the extended net present value (e–NPV); and (b) 50% PV share provided a 124% e–NPV gain when compared to the baseline cogeneration system with no PV shares. The analyses demonstrate that PV technology is a better hedging option than CCS against future market uncertainty and price volatility.

Comments: The Stark Realities of Energy Supply

Natural gas is considered the transition fossil fuel to move the world toward a meaningful reduction in global greenhouse-gas emissions. But the clampdown of Russia’s natural gas supplies shifted the market’s dynamics, and the repercussions are being experienced around the world. Europe is one of the hardest hit by this disruption in natural gas supplies. In a commentary posted on 18 July, Fatih Birol, executive director of the International Energy Agency, called for coordinated actions across Europe to prepare for the coming winter months and for worst-case scenarios of a complete shutoff of Russian gas and limited availability from other sources. Although Russia resumed a reduced flow of gas to Germany via the Nord Stream pipeline on 21 July, there is no certainty about the fluctuations or duration of the flow. Birol wrote, “Europe is now forced to operate in a constant state of uncertainty over Russian gas supplies, and we can’t rule out a complete cutoff.” The start of Europe’s winter heating season is 1 October. Birol called for action to fill European gas storage to adequate levels by reducing current gas consumption and to put that conserved gas into storage. High natural gas prices have led to gas conservation to some extent, but more efforts are required. He wrote that the gas required to be saved over the next 3 months “is in the order of 12 billion cubic metres—enough to fill about 130 LNG tankers,” equivalent to 424 Bcf. To lend some perspective to this, consider that US LNG exports averaged 10.1 Bcf/D in June. This reflects a 1.5 Bcf/D decrease from May because of a fire on 8 June that shut down Freeport LNG in Texas, according to the US Energy Information Administration (EIA). The agency expects US LNG exports to remain below average at 10.5 Bcf/D in the second half of this year. The Freeport LNG shutdown, and the resulting lower demand for LNG export gas, decreased the US benchmark Henry Hub natural gas spot price. It fell by $1.27/MMBtu to $8.16/MMBtu on 9 June and averaged $7.70/MMBtu in June. EIA expects the spot price to continue to decline to an average of $5.97/MMBtu in the second half of this year. European imports of LNG rose by more than 50% in the first 5 months of 2022 compared with the period a year earlier, according to Bernstein, a Wall Street research firm. Although a wide range of shippers have sent fuel to Europe, the major increases in its LNG imports have come from gas produced from US shale. The increased European demand also increased the price of its imported LNG to nearly nine times what it was about 2 years ago, and those higher prices enticed LNG shippers away from Asia. European futures prices have been around $40/MMBtu. The Wall Street Journal compared that to more than $200/bbl for oil, about twice the price for Brent crude, the international benchmark, as of 22 July. In most years, China, Japan, and South Korea have been the major markets for LNG. During the first 5 months of 2022, Asian imports fell by 8%—in part because of slowing demand from China caused by COVID-19 lockdowns. Birol’s commentary began with this sentence: “The world is experiencing the first truly global energy crisis in history.” For Europe, the Russian invasion of Ukraine also sparked a cost-of-living crisis when oil and gas prices soared. Crises, by definition, require immediate and effective actions.

Forecasting Natural Gas Spot Prices with Machine Learning

The ability to accurately forecast the spot price of natural gas benefits stakeholders and is a valuable tool for all market participants in the competitive gas market. In this paper, we attempt to forecast the natural gas spot price 1, 3, 5, and 10 days ahead using machine learning methods: support vector machines (SVM), regression trees, linear regression, Gaussian process regression (GPR), and ensemble of trees. These models are trained with a set of 21 explanatory variables in a 5-fold cross-validation scheme with 90% of the dataset used for training and the remaining 10% used for testing the out-of-sample generalization ability. The results show that these machine learning methods all have different forecasting accuracy for every time frame when it comes to forecasting natural gas spot prices. However, the bagged trees (belonging to the ensemble of trees method) and the linear SVM models have superior forecasting performance compared to the rest of the models.

Influencing Factors Analysis of Crude Oil Futures Price Volatility Based on Mixed-Frequency Data

This article takes into account the form of mixed data as well as the peak and thick tail characteristics contained in the data characteristics, expands the GARCH-MIDAS (Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling) model, establishes a new GARCH-MIDAS model with the residual term of the skewed-t distribution, and analyzes the influence factors of crude oil futures price volatility, which can better explain the changing laws of crude oil price volatility. The results show the following: First, the low-frequency factors include crude oil production, consumption, inventory, and natural gas spot price, and the high-frequency factors include on-market trading volume and off-market spot price, which can significantly explain the volatility of oil price. Second, low-frequency factors include crude oil inventory, consumption, crude oil production, and speculative factors, and high-frequency factors include crude oil spot price and substitute prices. The increase in the volatility of trading volume is significantly positively correlated with oil price volatility, and the overall volatility model outperforms the horizontal effect model. Third, from the perspective of the combined effect of a single factor level and volatility, we find that supply and demand are the low-frequency factors; the trading volume of on-market factors, natural gas price, and crude oil spot price of off-market factors, among the high-frequency factors, are the most important factors affecting oil price volatility. Fourth, from the perspective of high-frequency and low-frequency effects combined, there is no significant difference between the various factor models, which shows that the mixed effect model of high and low frequency models has advantages in terms of the stability of the estimation results.

Is there a bubble in the shale gas market?

In this paper, we investigate the existence of bubbles in the shale gas sector. We construct a shale gas index to reflect the market’s expectations for the future growth of the shale gas and separate the shale gas spot price from the spot market to reflect current demand. The results from the Generalized Supremum Augmented Dickey-Fuller and Supremum Kapetanios, Shin and Snell test show strong evidence of multiple episodes of the explosive phenomenon in the shale gas sector. Moreover, we set up the date stamp at both the outset and the end of these explosive behaviors. We find the bubbles are inconsistent with the natural gas spot price and WTI crude oil price. For shale gas, our empirical results present that there is a similar trend among the futures market, stock market, and the spot market. Moreover, in the whole energy market, the explosive bubble behavior which we find in the shale gas sector before 2009 is consistent with the natural gas and crude oil prices, which verifies the shale gas price coherence with other main energy productions.

Likelihood inference for Markov switching GARCH(1,1) models using sequential Monte Carlo

Markov switching (MS-)GARCH(1,1) models allow for structural changes in volatility dynamics between a finite number of regimes. Since the regimes are not observed, computation of the likelihood requires integrating over an exponentially increasing number of regime paths, which is intractable. An existing smooth likelihood estimation procedure for sequential Monte Carlo (SMC), that is currently limited to hidden Markov models with a one-dimensional state variable, is modified to enable likelihood estimation and maximisation for MS-GARCH(1,1) models, a model which requires two dimensions, volatility and regime, to evolve its hidden state process. Furthermore, the modified SMC procedure is shown to be easily adapted to fitting MS-GARCH(1,1) models even when there are missing observations. The proposed methodology is validated with simulated data and is also illustrated with analysis of two financial time series, the daily returns on the S&P 500 index and on the Henry Hub natural gas spot price, with the latter series containing a gap caused by shutdown in response to hurricane Rita in 2005.

Daily natural gas price forecasting by a weighted hybrid data-driven model

With the role of natural gas gaining increasing importance in the transition of the world energy system and addressing global climate change, accurate prediction of the price of natural gas becomes crucially important. This paper first introduces three widely used individual data-driven models, i.e., support vector regression (SVR) and long-term and short-term memory network (LSTM), and a modified data-driven model, i.e., the improved pattern sequence similarity search (IPSS). A new weighted hybrid data-driven model based on these three models is then proposed. To train the model, data regarding the daily natural gas spot price in the U.S. prior to June 2018 are used and the model's prediction ability is tested using data from June 2018 to May 2019. The results show that the new IPSS model can predict the daily price of natural gas accurately. In a comparison of prediction errors with other individual models, the proposed hybrid model demonstrated the highest prediction ability of all of the investigated models.

The liquefied natural gas spot market and valuation of the rerouting option

The liquefied natural gas (LNG) market has experienced remarkable changes in the last few years, with long-term contracts being replaced by short-term ones and optionalities being granted by suppliers in the context of a large increase in natural gas production. Flexible LNG contracts give buyers the option to redirect their cargo if they identify a higher spot price at a point different from the original destination. The goal of this paper is twofold: (1) to describe the new outlook of LNG markets, which has become more and more spot-centric, with Asian LNG futures bringing transparency to spot and forward prices; and (2) to address the valuation of the rerouting option, a number that must be accounted for by the buyer when assessing the profitability of a given cargo. As an example, we apply the rerouting option valuation methodology to a scenario in which the supplier is the United States, the original destination is Germany and the alternative destination is Japan. We assume the rerouting of the vessel takes place when the cargo reaches the waters of Germany. This approach can be used for any group of three countries by adjusting the transportation costs. Our paper also depicts the value of the option as a function of the volatility of the natural gas spot prices in the alternative destination.

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

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.

Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm

Natural gas is often described as the cleanest fossil fuel. The consumption of natural gas is increasing rapidly. Accurate prediction of natural gas spot prices would significantly benefit energy management, economic development, and environmental conservation. In this study, the least squares regression boosting (LSBoost) algorithm was used for forecasting natural gas spot prices. LSBoost can fit regression ensembles well by minimizing the mean squared error. Henry Hub natural gas spot prices were investigated, and a wide range of time series from January 2001 to December 2017 was selected. The LSBoost method is adopted to analyze data series at daily, weekly and monthly. An empirical study verified that the proposed prediction model has a high degree of fitting. Compared with some existing approaches such as linear regression, linear support vector machine (SVM), quadratic SVM, and cubic SVM, the proposed LSBoost-based model showed better performance such as a higher R-square and lower mean absolute error, mean square error, and root-mean-square error.

Pseudo-exponential distribution and its statistical applications in econophysics

In generalized measure theory, $$\sigma $$ - $$\oplus $$ -measure is a generalization of the classical measure defined on a pseudo-addition. In this paper, the class of pseudo-exponential distributions based on a class of $$\sigma $$ - $$\oplus $$ -measure is introduced. Some examples of this class are investigated. Then by two real data sets obtained from the last three decades of oil, and the last two decades of the daily natural gas spot prices, we show that the pseudo-exponential distribution is better fitted than exponential distribution using the AIC and BIC information criteria.

Testing Co-Volatility spillovers for natural gas spot, futures and ETF spot using dynamic conditional covariances

There is substantial empirical evidence that energy and financial markets are closely connected. As one of the most widely-used energy resources worldwide, natural gas has a large daily trading volume. In order to hedge the risk of natural gas spot markets, a large number of hedging strategies can be used, especially with the rapid development of natural gas derivatives markets. These hedging instruments include natural gas futures and options, as well as Exchange Traded Fund (ETF) prices that are related to natural gas stock prices. The volatility spillover effect is the delayed effect of a returns shock in one physical, biological or financial asset on the subsequent volatility or co-volatility of another physical, biological or financial asset. Investigating volatility spillovers within and across energy and financial markets is a crucial aspect of constructing optimal dynamic hedging strategies. The paper tests and calculates spillover effects among natural gas spot, futures and ETF markets using the multivariate conditional volatility diagonal BEKK model. The data used include natural gas spot and futures returns data from two major international natural gas derivatives markets, namely NYMEX (USA) and ICE (UK), as well as ETF data of natural gas companies from the stock markets in the USA and UK. The data used for the empirical analysis is from 14 May 2007 to 15 April 2016, incorporating 2330 observations. The empirical results show that there are significant spillover effects in natural gas spot, futures and ETF markets for both USA and UK. Such a result suggests that both natural gas futures and ETF products within and beyond the country might be considered when constructing optimal dynamic hedging strategies for natural gas spot prices.

Technology Focus: Natural Gas Processing and Handling

Technology Focus Despite a dip earlier in the year, natural-gas prices remained steady over 2017, averaging $2.99/million Btu. According to the US Energy Information Administration (EIA), the decrease in natural-gas price in 2017 was because of the warmer winter and lower gas usage for power generation, which led to lower demand. According to the latest figures from the International Energy Agency, the year-over-year aggregate gross consumption (production plus imports minus exports and stock changes) for US natural gas was up 5.7% in October 2017. Rising demand from Mexico coupled with lower gas prices and greater pipeline capacity resulted in US exports increasing by 0.4 Bcf/D in 2017, and they are expected to increase by a further 0.6 Bcf/D in 2018. In 2017, the US also became a net exporter for the first time since 1957 when taking annual figures into account. Early 2018 has been a different story, however, with natural-gas prices already hitting a couple of peaks (of greater than $5/million Btu according to the Henry Hub Spot Price history). In the US, colder-than-expected temperatures contributed to peaks in demand, but, at the time of writing, the prices have settled down to the pre-“bomb cyclone” levels, with current natural-gas prices almost mirroring those in 2017 (at $2.868/million Btu). The Henry Hub Natural Gas Spot Prices are projected to average $2.88/million Btu in 2018. The EIA predicts that natural-gas usage for residential and commercial purposes will likely increase by 1.3 Bcf in 2018. Industrial consumption is also expected to rise by 1.2% this year, with an average of 21.7 Bcf/D. Dry-natural-gas production is expected to rise to 6.9 Bcf/D (9.3%). Most of the growth in natural gas is expected to be in the Marcellus, Utica, and Permian basins. Similarly, for liquefied natural gas (LNG), the forecasts are rosy, with an expected average rate of 3.0 Bcf/D in 2018. This is because of an expected increase in production from the Cove Point terminal in Maryland and additional facilities coming online this year—namely, the Elba Island facility in Georgia and the Free-port LNG plant in Texas. A novel hybrid solvent for acid-gas removal improves the handling of sour natural gas by effectively dealing with mercaptans present without the need for modifying facilities and with no adverse effects on the sulfur-recovery units down the line. This enables the whole process to be more economical and efficient while meeting the necessary health, safety, and environment requirements. To learn more, visit the 2018 SPE Annual Technical Conference and Exhi-bition in Dallas on 24–26 September and the 2018 SPE Asia Pacific Oil and Gas Conference in Brisbane, Australia, on 23–25 October. Recommended additional reading at OnePetro: www.onepetro.org. SPE 186394 Investigation of Failed LNG Boil-Off Gas-Transfer-Line-Expansion Bellows Using Finite-Element-Analysis Approach by E.Y. Pratama, PT Badak LNG SPE 186932 From Sandface to Processing Plant, an Integrated View of an Operating Envelope in a Gas/Condensate System by M. Sueiro, Repsol, et al. SPE 188347 Overcoming Challenges To Reduce H2S Content in Sales-Gas-Transmission Pipelines by JagannathRao PrakashRao Allamaraju, Abu Dhabi Gas Industries, et al.

Trends in the natural gas pricing mechanism reform in China: a perspective of dependency between China and the US natural gas markets

Establishing a natural gas market pricing mechanism is crucial to Chinese market reforms. This paper explores the achievements of pricing mechanism reform since 2013 and examines the current market trends. We used copula function to analyse the relationship between liquid natural gas arrival price in Guangzhou and the Henry Hub natural gas spot price in the USA from 2013-2017. The correlations between the natural gas market prices increased from 2013-2015, with rank correlation coefficient reaching a maximum of 0.73 in 2015; Although the correlations were negative from 2016-2017, the decreasing negativity indicates China's market price converged to that of the USA from 2013-2015 and had a temporary separation in 2016 and 2017. It was concluded that while China's natural gas pricing mechanism is becoming more dependent on market mechanisms, it needs to be strengthened by learning from the US natural gas development process.

Trends in the natural gas pricing mechanism reform in China: a perspective of dependency between China and the US natural gas markets

Establishing a natural gas market pricing mechanism is crucial to Chinese market reforms. This paper explores the achievements of pricing mechanism reform since 2013 and examines the current market trends. We used copula function to analyse the relationship between liquid natural gas arrival price in Guangzhou and the Henry Hub natural gas spot price in the USA from 2013-2017. The correlations between the natural gas market prices increased from 2013-2015, with rank correlation coefficient reaching a maximum of 0.73 in 2015; Although the correlations were negative from 2016-2017, the decreasing negativity indicates China's market price converged to that of the USA from 2013-2015 and had a temporary separation in 2016 and 2017. It was concluded that while China's natural gas pricing mechanism is becoming more dependent on market mechanisms, it needs to be strengthened by learning from the US natural gas development process.