Natural Gas Futures Research Articles

Price discovery redux—Analyzing energy spot and futures prices using a dynamic programming approach

We employ dynamic time warping (DTW), a non-parametric pattern recognition technique based on a dynamic programming algorithm, to analyze whether futures markets for crude oil and natural gas have facilitated price discovery over the last decade. Should futures prices absorb and reflect information before spot prices, they will move first and lead spot prices, suggesting that they dominate spot prices and play an important role in price discovery. The results show that natural gas futures prices led spot prices more frequently between 2019 and 2023 than during the five years preceding this turbulent period. In the case of crude oil, however, futures prices lagged spot prices more often than leading them. The evidence suggests that futures prices have not consistently fulfilled their price discovery role in the two energy markets. The results also indicate that short-term futures contracts play a more dominant role in price discovery than long-term contracts. Additionally, we demonstrate the advantages of DTW: it lends itself well to analyzing small samples with different orders of integration; it can discover linear and nonlinear relationships between time series; notably, it can detect period-to-period changes in the duration and direction of lead-lag associations between two series and present the results intelligibly.

Dynamic Correlations Between Carbon Futures and Energy Futures Markets

An essential instrument for reducing carbon emissions worldwide is the carbon market. Comprehending the dynamic relationships between carbon and energy futures prices is crucial as carbon trading picks up promote globally. This research examines how two energy futurescrude oil and natural gasinteract with carbon futures in the American and European markets between November 21, 2013, and March 28, 2024. We examine correlations between futures returns across different regions using the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model. Our findings reveal significant, albeit fluctuating, correlations influenced by external market events. Interestingly, compared to crude oil futures, there is a stronger dynamic link between carbon futures and natural gas futures, especially in the European market, demonstrating strong long-term persistence.

Simmering tensions on the Russia–Ukraine border and natural gas futures prices: identifying the impact using new hybrid GARCH

Focusing on the Russia–Ukraine war, this paper investigates natural gas futures volatilities. Applying several hybrid GARCH and EGARCH models, which innovatively incorporate both fat-tailed distribution errors and structural breaks, we derive the following new evidence. First, our hybrid modeling approach is effective in timely capturing the natural gas futures volatility spike when tensions simmered on the Russia–Ukraine border. Second, the hybrid modeling approach is effective for not only GARCH modeling but also EGARCH modeling. Third, the volatility estimates from our hybrid models have predictive power for the volatilities of nonhybrid models. Fourth, the volatility estimates from the nonhybrid models lag behind the volatilities of our hybrid models.

Predicting energy prices based on a novel hybrid machine learning: Comprehensive study of multi-step price forecasting

Accurate prediction of energy prices is crucial to the development of energy security and environmental policies in various countries. This paper proposes a novel multi-step prediction hybrid model with genetic algorithm for variational mode decomposition, improved complete ensemble empirical modal decomposition with adaptive noise, bidirectional gated recurrent unit, temporal convolutional network, and multi-layer perceptron (GVMD-ICEEMDAN-BIGRU-TCN-MLP) for predicting carbon and natural gas futures prices. First genetic algorithm (GA) is used to fix the parameters of VMD model, the carbon and natural gas prices are decomposed into subsequences. Then the difference between the original series and the VMD after decomposition is further decomposed into subseries using ICEEMDAN. Next, the highest frequency series is predicted using the MLP model, and other subsequences are predicted using the BIGRU-TCN model. Finally, each predicted value is added linearly to determine the final result of steps 1, 3, and 5 of the entire forecasting process. According to the experimental results, it is shown that the model has lower prediction errors than the comparison model under mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and modified Diebold-Mariano test (MDM). The good prediction results of the novel hybrid model are demonstrated in multi-step ahead integrated prediction experiments, especially in the experiments with 1-step ahead prediction, as well as in the experiments with varying training ratios.

Energy Price Inflation, Geopolitical Risk, and Bitcoin Dependence Structure: Evidence from BRICS

ABSTRACT This study examines the co-movement between geopolitical risk (GPR), energy price, and bitcoin (BTC) in BRICS countries, namely Brazil, Russia, India, China, and South Africa. Previous studies have focused on the impact of GPR on the volatility and risk premium of BTC investment. However, very limited studies have focused on integrating BTC as an extension of the mix of GPR on the co-movement with energy price. The analysis is based on monthly data of GPR index for BRICS countries, brent oil futures, natural gas futures and BTCs covering the period between March 2012 and Jun 2021. We employ the Bayesian graphical structural vector autoregressive model and time-varying parameter vector autoregressions-based dynamic connectedness to investigate the network-dependence structure. This research project provides useful empirical evidence for assessing the impact of both BTC and GPR on energy prices. Nonetheless, it will also be informative about the likelihood of co-movements occurring at different stages.

Energy price shocks and stock market volatility in an energy-importing country

We examine volatility dynamics in the Korean market using heterogeneous autoregressive models with exogenous covariates. The COVID-19 pandemic and the Russia–Ukraine War have caused substantial fluctuations in energy prices. We assess how these energy shocks affect stock market-implied volatility in Korea, a representative energy-importing country. During the pre-pandemic period, domestic and U.S. market factors affect Korea's volatility dynamics, whereas crude oil and natural gas futures prices have little explanatory power for these dynamics. In contrast, during the pandemic (war), oil (natural gas) futures prices become the key explanatory variable, and other market factors lose their explanatory power.

Transmission Mechanism and Extreme Time-Varying Spillover Effects between China and International Gas Markets

This study uses the quantile connectedness model to study the transmission mechanism between China and the international natural gas market from August 2017 to February 2024. The time-varying spillover effects under extreme market conditions. The empirical results emphasize the following key points: (a) China mainly acts as an information receiver in the overall market. (b) The spillover effect of the European natural gas market is significantly stronger during the extreme downturn, indicating that the spillover effect is asymmetric. (c) The natural gas futures market plays a central role in market dynamics that affect upward return shocks. (d) System connectivity is highly sensitive to market conditions and increases significantly under extreme market conditions. According to the research results, we should make more efforts to promote the liberalization and marketization of the domestic natural gas market, and strive to improve the bargaining power in the international market to reduce the import premium level. Improve energy efficiency and reduce China ’s dependence on natural gas imports; from the perspective of the global natural gas market, risk contagion between markets should be alleviated under different market conditions and a differentiated risk early warning mechanism should be established.

Dynamic impact of the COVID-19 lockdown intervention policies on network structure of energy futures return connectedness

This work investigates the dynamic impact of COVID-19 lockdown policies, implemented to curb the pandemic's spread, on the structure of the energy futures return connectedness network (EFRCN). Firstly, we measured the connectedness of 20 energy futures return series using the time-varying parameter vector autoregression (TVP-VAR) frequency connectedness approach and constructed a directed multi-layer dynamic EFRCN. Secondly, we developed a novel statistical indicator, the ratio of average vertex out-strength to average vertex in-strength, to represent the spillover intensity of a specific network layer. Finally, we utilized the pruned exact linear time (PELT) algorithm to pinpoint structural changepoints of COVID-19 lockdown policies and explored the change in the EFRCN structure before and after these changepoints. The empirical findings demonstrate that the scales of population and gross domestic product (GDP) impacted by the COVID-19 lockdown positively drive the average vertex strength, density, and clustering coefficient of the EFRCN, as well as the return spillover intensity of oil and power futures markets, respectively. Conversely, the regression results exhibit noteworthy negative causal relationships between the scales of population and GDP impacted by the COVID-19 lockdown and the return spillover intensity of coal and natural gas futures markets for non-periodic and short-term networks. Furthermore, the empirical results illustrate distinct structural changes in the evolution of global indicators of the non-periodic and short-term networks at the changepoints of COVID-19 lockdown policies before June 2020. To sum up, COVID-19 lockdown intensification increases the clustering of the EFRCN and significantly enhances the net spillover effects of oil and power futures markets within the EFRCN. Moreover, the changes in COVID-19 lockdown policies significantly influence the EFRCN's structure.

What has the strongest connectedness with clean energy? Technology, substitutes, or raw materials

This paper examines the connectedness of clean energy with technology, substitutes (crude oil and natural gas), and raw materials (silver, platinum, copper, corn, and soybean). This is conducted from a systemic perspective, using the quantile time–frequency connectedness approach. The results reveal that clean energy and technology stocks demonstrate the strongest pairwise connectedness across all quantile levels in the short, medium, and long terms. Meanwhile, fossil energy commodities (as substitutes) and metal and agricultural commodities (as raw materials) exhibit relatively intensive connectedness, with clean energy stocks only at the lower and upper quantiles in the short term. Furthermore, connectedness among the markets under examination is significantly enhanced during periods of financial turmoil, as was the case during the European debt crisis and the COVID-19 pandemic. Finally, the minimum connectedness portfolio analysis indicates that natural gas futures offer the highest hedge effectiveness, and technology stocks offer the lowest hedge effectiveness across frequencies and quantiles. This finding suggests that optimal portfolios will have a larger proportion of natural gas futures. These findings have significant implications for both investors and policymakers.

A Study on Co-movements and Information Spillover Effects Between the International Commodity Futures Markets and the South Korean Stock Markets: Comparison of the COVID-19 and 2008 Financial Crises

Purpose - This paper aims to compare and analyze the co-movements and information spillover effects between the international commodity futures markets and the South Korean stock markets during the COVID-19 and the 2008 financial crises.
 Design/methodology - The DCC-GARCH model is used in the co-movements analysis. In contrast, the BEKK-GARCH model is used to evaluate information spillover effects. The statistical data used is from January 1, 2005, to December 31, 2022. It comprises the Korea Composite Stock Price Index data and daily international commodity futures prices of natural gas, West Texas Intermediate crude oil, gold, silver, copper, nickel, soybean, and wheat.
 Findings - The results of the co-movement analysis were as follows: First, it was shown that the co- movements between the international commodity futures markets and the South Korean stock markets were temporarily strengthened when the COVID-19 and 2008 financial crises occurred. Second, the South Korean stock markets were shown to have high correlations with the copper, nickel, and crude oil futures markets. The results of the information spillover effects analysis are as follows: First, before the 2008 financial crisis, four commodity futures markets (natural gas, gold, copper, and wheat) were shown to be in two-way leading relationships with the South Korean stock markets. In contrast, seven commodity futures markets, except for the natural gas futures market, were shown to be in two-way leading relationships with the South Korean stock markets after the financial crisis. Second, before the COVID-19 crisis, most international commodity futures markets, excluding natural gas and crude oil future markets, were shown to have led the South Korean stock markets in one direction. Third, it was revealed that after the COVID-19 crisis, the connections between the South Korean stock markets and the international commodity futures markets, except for natural gas, crude oil, and gold, were completely severed.
 Originality/value - Useful information for portfolio strategy establishment can be provided to investors through the results of this study. In addition, it is judged that financial policy authorities can utilize the results as data for efficient regulation of the financial market and policy establishment.

Will the “Responsibly Sourced Gas” Initiative Secure the Future of Natural Gas?

Years ago, during the 2009–2014 time period, as hydraulic fracturing—the unfortunately named “fracking”—was the red‐hot target of critics of the natural gas industry, it became very clear that it was critical to satisfy public concerns surrounding the safety and environmental impact of the fracking process. Variations in the behavior of the nation's huge population of natural gas producers rendered the industry vulnerable to its weakest members. In the best‐run companies, usually major and large independent companies, there was an intense focus on clean and safe development, dealing with contaminated waste water as well as the sourcing of fracturing water, and in particular ensuring the absolute integrity of underground facilities to prevent the process from ever interacting with drinking water aquifers. Best practices also involved the overall management of the impact of site preparation, the operations themselves, including noise pollution and long‐term site management. I argued frequently for the equivalent of a “Good Housekeeping Seal of Approval” for the most responsible producers to have a leg up in permitting, and a market (and price) advantage over others who could not show the same level of responsibility.

Spillover relationship between different oil shocks and high- and low-carbon assets: An analysis based on time-frequency spillover effects

In this paper, the spillover relationship between different oil shocks and high- and low-carbon assets is investigated through DY and BK spillover methods. It is found that the spillover relationships between them are heterogeneous, with WTI crude oil futures and heating oil futures being highly affected by oil demand and supply shocks, and clean energy sector stocks being highly affected by oil risk shocks. The spillover relationship between them changes at different time frequencies. For high- and low-carbon assets, more attention should be paid to protecting against risks from the same assets. Natural gas futures are little affected by spillovers.

International and Chinese energy markets: Dynamic spillover effects

On the basis of a time-varying parametric vector autoregressive model (TVP-VAR) with spillover index model and wavelet coherence analysis, we study spillover effects of price fluctuations in global and Chinese energy markets. The empirical results indicate that the price trends of coal, crude oil, and natural gas in China and the world are closely interlinked. Extreme events have significantly amplified energy market fluctuations' spillover effect. Additionally, international energy market is a net exporter of fluctuations and spillovers, especially crude oil, and natural gas, which is consistent with China's energy import structure. There are time-varying characteristics and cross-effects between spillover effects in different energy markets. Finally, rising shipping costs, increased stock market volatility, and geopolitical risks are more likely to exacerbate cross-market risk spillovers, while exchange rate depreciation and economic policy uncertainty can dampen risk spillovers to some extent. Based on our findings, we recommend the following actions: build a comprehensive energy price warning mechanism, establish and improve the natural gas futures market, set up a more diversified investment portfolio and dynamically adjust investment strategies, and more flexible and precise policy combinations.

Global energy supply risk: Evidence from the reactions of European natural gas futures to Nord Stream announcements

Motivated by recent geopolitical challenges to globalization, finance scholarship has a growing interest in the risks to economies of key supply chains. We explore the effect of Nord Stream pipeline development on daily natural gas prices in Europe. Reviewing a comprehensive list of 24 announcements related to Nord Stream pipeline developments, stoppages, and sanctions for 2013–2022, we investigate their direct impact on the volatility and returns of Dutch TTF natural gas futures. Employing bootstrap quantile regression, we find that the effects of such announcements are highly significant, especially during periods of low TTF volatility and returns distributions. Moreover, results show that both COVID-19 and the Russia-Ukraine war strenghtened the correlation between Nord Stream pipeline announcements and TTF market reactions, consistent with energy markets pricing geopolitical risks. Results, reflective of informed trading, warn of concentration risk in European gas markets, suggesting the need for policy actions to reduce supply-chain dependence. Taken together, we support the importance of diversifying both natural gas supply infrastructures and providers to mitigate geopolitical risk exposures and associated welfare costs to the real economy.

TBDQN: A novel two-branch deep Q-network for crude oil and natural gas futures trading

Algorithmic trading plays a significant role in the trade of crude oil and natural gas futures. In this paper we propose a novel deep reinforcement learning (DRL) algorithm, dubbed two-branch deep Q-network (TBDQN), to automatically produce consistently profitable and robust trading signals in crude oil and natural gas futures markets. The first branch exploits long-short-term memory (LSTM) module to discover potential features hidden behind many technical indicators; the second branch extracts intrinsic features from futures contracts, trading positions and OHLCV using deep neural network. The extracted features are fused together to form the state vector of Q-leaning. In order to facilitate the training of the TBDQN model learning, we design a novel reward function by incorporating both immediate and long-term rewards. Compared to other popular methods, the proposed algorithm demonstrates excellent performance on the evaluation criteria of annualized return and Sharpe ratio in the oil and gas futures markets, which proves the effectiveness of our method.

A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling

This work studies the term structure dynamics in the natural gas futures market, focusing on the Dutch Title Transfer Facility (TTF) daily futures prices. At first, using the whole dataset, we compared the in-sample fitting performance of three models: the four-factor dynamic Nelson–Siegel–Svensson (4F-DNSS) model, the five-factor dynamic De Rezende–Ferreira (5F-DRF) model, and the B-spline model. Our findings suggest that B-spline is the method that achieves the best in-line fitting results. Then, we turned our attention to forecasting, using data from 20 January 2011 to 13 May 2022 as the training set and the remaining data, from 16 May to 13 June 2022, for day-ahead predictions. In this second part of the work we combined the above mentioned models (4F-DNSS, 5F-DRF and B-spline) with a Nonlinear Autoregressive Neural Network (NAR-NN), asking the NAR-NN to provide parameter tuning. All the models provided accurate out-of-sample prediction; nevertheless, based on extensive statistical tests, we conclude that, as in the previous case, B-spline (combined with an NAR-NN) ensured the best out-of-sample prediction.

The impact of the Russia–Ukraine war on the United States natural gas futures prices

PurposeThis paper attempts to investigate the impact of the Russia–Ukraine war on the returns and volatility of the United States (US) natural gas futures market.Design/methodology/approachThe study uses secondary data of 996 trading day provided by the US Department of Energy and investing.com websites and applies the event study methodology in addition to the generalized autoregressive conditional heteroscedastic (GARCH) family models.FindingsThe findings from the exponential EGARCH (1,1) estimate are the best indication of a significant positive effects of the Ukraine–Russia war on the returns and volatility of the US natural gas futures prices. The cumulative abnormal returns (CARs) of the event study show that the natural gas futures prices reacted negatively but not significantly to the Russian–Ukraine war at the event date window [−1,1] and the [−15, −4] event window. CARs for the longer pre and post-event window display significant positive values and coincides with the standard finance theory for the case of the US natural gas futures over the Russia–Ukraine conflict.Originality/valueThis is the first study to examine the impact of the Russia–Ukraine war on natural gas futures prices in the United States. Thus, it provides indications on the behavior of investors in this market and proposes new empirical evidence that help in investment analyses and decisions.

Predicting natural gas futures’ volatility using climate risks

In this paper, we examine the tracking and predictive power of two kinds of climate risks—namely, climate policy uncertainty (CPU) and climate-related disasters—on the price volatility of natural gas futures. The GARCH-MIDAS model was adopted to incorporate daily natural gas futures prices with monthly CPU indices and disaster frequencies. The empirical results showed a robust predictive relationship between disaster frequency and natural gas price volatility under both in-sample and out-of-sample scenarios, while combining the CPU index with other predictors could not improve the out-of-sample forecasting performance. We believe these findings could provide insights for traders and market regulators.

Riding the Roller Coaster—Today's Natural Gas Market and the Future

The beginning of 2023 has witnessed the biggest decline in natural gas prices in a long time. Production has been at record highs, an exceptionally warm start to January suppressed supply demand, and liquefied natural gas (LNG) exports have been down since June 2022 when Freeport LNG's export facility went offline due to a fire at its facility. The CME/NYMEX February natural gas futures contract slid to an 18‐month low of $2.94/MMBtu and expired at $3.109/MMBtu, down 54 percent from where the contract closed just two months earlier in December 2022. In other words, market prices plunged, and in the middle of the winter—usually the strongest period for natural gas sales and prices. The March contract extended the slide to a 20‐month low of $2.677/MMBtu. Freeport's eventual return will restore existing LNG export capacity, thus picking up demand levels, and by the time of publication of this column may be back in service. However, there is no new LNG export capacity due online this year—for the first time since 2016. After one of the tightest natural gas markets of the last decade in 2022, which pushed prices much higher than they had been for a long time, other than during the short but fierce Winter Storm Uri experience in 2021, things they are a'changing. The stage is set for one of the most oversupplied markets the industry has seen in years. However, in keeping with the natural gas industry's history of keeping traders on their toes, it looks as if we are reaching the end of the steady oversupply we have experienced since 2009, when the Shale Revolution swarmed in to move the entire industry from shortage to abundance, and enabled U.S. energy independence to take root. How did we get here, and how does the future look for natural gas supply and prices?

Natural gas and the utility sector nexus in the U.S.: Quantile connectedness and portfolio implications

Given that natural gas is a vital input for the U.S. utility sector, this study empirically investigates the return connectedness between the natural gas and utility stocks in the U.S. market. Using the quantile connectedness approach, we show that the nexus between natural gas and utility stocks is more pronounced at the tails compared to the central of the conditional distribution. The return connectedness indices are time-varying with a net receiver role of natural gas and driven by various macro-variables. Finally, our portfolio implication analyses with alternative tail risk measures suggest that it can be more beneficial for risk-adverse investors to allocate substantial weights into the electricity utility stocks in normal market conditions. However, during the COVID-19- induced recession, it is critical to shift more fund to the natural gas futures to reduce tail risks.