Natural Gas Futures Research Articles

The Future of Natural Gas in California

California has been known for decades as being one of the most environmentally progressive states in the country. Its fuel efficiency standards for cars ultimately led to tighter federal corporate average fuel economy (CAFE) standards. California's embrace of natural gas to phase out coal power plants started well before such federal efforts. And now, the state has aggressive renewables targets that are making progress toward a low‐carbon energy economy. Of course, in many circles around the US one only need say “California” and the room dissolves in laughter. Budget crises, high taxes, wildfires followed by mudslides and strange election processes all lead to many people scoffing at the state. However, it is the fifth largest economy in the world. On the whole, California's energy practices and policies have yielded many successes that have caught on worldwide.

Timing differences in the impact of Covid-19 on price volatility between assets

We empirically examine the impacts of Covid-19 on asset price volatilities by focusing on the timing. This paper has three contributions. First, we propose a new Covid-19 dependent regime-switching volatility model for the examination. Second, results show a shift to a higher price volatility regime from a lower one for financial assets and commodities after late February 2020 when Covid-19 spread all over the world, but the timing of the impacts varies from immediate timing for the S&P 500, the FTSE 100, the COMEX gold and silver futures to the delayed timing for the ICE Brent crude oil futures followed by the timing for the ICE UK natural gas futures. Third, we find the sensitivity of Covid-19 information to the regime switch differs between financial assets and precious metal ones which have the immediate impacts: the infection speed, i.e. the changes in the number of Covid-19 infected individuals, enhance the impacts on the tendency to a high price volatility regime for the S&P 500 and the FTSE 100; both the infection speed and the number of the deaths mitigate those impacts for the gold and silver futures, respectively during a turmoil period due to Covid-19, suggesting that the gold and silver markets are functioning as risk-hedging safety assets alternative to financial assets during Covid-19 turmoil.

Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on multi-objective version of chaos game optimization algorithm

Effective crude oil and natural gas futures price forecasting is a crucial endeavor for financial energy markets and is also a challenging work due to the nonlinear and fluctuant characteristic of futures price time series. Most existing researches have failed at the consideration of both linear and nonlinear information, optimal sub-model selection, and interval forecasting. To bridge these gaps, a novel decomposition-selection-ensemble forecasting system is proposed to perform deterministic prediction and interval forecasting in this study, which is constituted by data decomposition method, optimal sub-model selection strategy, proposed multi-objective version of chaos game algorithm, and multiple forecasting models. The proposed forecasting system prominently prompted the forecasting accuracy and stability of energy futures price, and improved the applicability at dealing with different data characteristic. Empirical results based on energy futures price demonstrated that the point forecasting and interval forecasting results obtained from the proposed forecasting system are more reliable and stable relative to other comparative models; thus, it can provide useful references for national economic policies and operators in financial energy markets.

Dynamic frequency relationships and volatility spillovers in natural gas, crude oil, gas oil, gasoline, and heating oil markets: Implications for portfolio management

This paper examines the dynamic frequency co-movements and volatility spillovers between crude oil, gas oil, gasoline, heating oil, and natural gas futures markets during the global financial crisis and European crisis (GFC & ESDC), recent oil price crash, and COVID-19 pandemic crisis. We apply the spillover index by Diebold and Yilmaz (2012) and wavelet methods. The results show significant risk spillovers among the leading energy futures markets. Moreover, the spillovers are intensified during the financial crisis, oil crisis and COVID-19 outbreak. WTI crude oil is the highest net contributor of volatility spillovers to the other markets, whereas the other energy markets are net receivers of spillovers for the different sub periods, with the exception of natural gas before the GFC and during COVID-19 as well as Brent oil during COVID-19. Furthermore, the results show significant integration and multiscale co-movements among energy futures. Natural gas asset offers better diversification benefits to crude oil, heating oil, gasoline and gas oil under short term. Finally, the diversification gains diminish as scales rise. The optimal weight, hedge ratios and hedging effectiveness are time varying and crises-sensitive. These findings have significant implications for energy traders and policymakers to inform their decision-making.

Valuation of Energy Security for Natural Gas—European Example

Recently there has been an ongoing discussion about energy security. This has been caused by tensions affecting international relations, and the emergence of new geopolitical threats. As one of the main sources of primary energy, natural gas is an obvious subject of interest in this discussion. In Europe, the natural gas market is rapidly evolving, which has resulted in a lack of clarity regarding who is responsible for the security of the gas supply. It is not clear now how to measure the security of the gas supply in economic estimates and by whom that security should be financed. In this paper, the authors present an approach which can be used for valuation of energy security concerning the security of natural gas storage using stochastic modelling based on the mathematical model of the “Newsvendor problem”. The valuation is made from the point of view of countries and considers their individual attitudes to the risk of disruption of deliveries, which is a novel approach to the problem. The authors believe that the current level of storage capacities, as compared to the demand for natural gas, can show the attitude of each country to the risk and potential cost of stockout. In line with this belief, the target value in the model is not the optimal level of inventory, but the cost of stockout. The results show significant variations in the assessment of the risk. The future of natural gas as an important fuel and source of primary energy in Europe is not clear and unfavorable changes have been sped up by the COVID-19 pandemic. Gas (energy) companies in Europe are turning to decarbonization and green energy, and the pandemic has accelerated these changes. European energy companies used to see the use of natural gas as a transition fuel and a key component of their long-term decarbonization strategies, but now they are switching to multi-energy models through massive investments in renewables and hydrogen. Experts expect that gas will remain an important part of Europe’s energy supply, but it may be gradually replaced by hydrogen and renewables.

The Dynamic Volatility Connectedness Structure of Energy Futures and Global Financial Markets: Evidence From a Novel Time–Frequency Domain Approach

We consider directional volatility connectedness among energy markets and financial markets over time and frequencies simultaneously during the period 2007–2018. We utilize and expand Barunik and Krehlik (J Financ Econom 16:271-296, 2018) connectedness measurements using HVAR in order to achieve a better perspective of energy markets. Our results indicate that during a crisis, the connectedness among markets increases dramatically. Furthermore, our findings support that markets are mostly driven by short-term factors and are highly speculative. Among energy markets, Natural Gas Futures contribute the least to other markets in all time frames. Besides, London Gas Oil Futures and Heating Oil Futures collaborate. Currencies and Natural Gas Futures are suitable choices for portfolio managers to hedge their risks especially in the long run. The findings of this article can offer new insights to policymakers about the mechanism of connectedness among different markets and international investors.

Dynamic volatility spillovers across oil and natural gas futures markets based on a time-varying spillover method

This paper analyzes dynamic volatility spillovers between four major energy commodities (i.e., crude oil, gasoline, heating oil and natural gas) in the oil-natural gas future markets. We construct a time-varying spillover method by combining the TVP-VAR-SV model and the spillover method of Diebold and Yilmaz (2009, 2012, 2014). We use the spillover method to obtain time-varying total, directional and pairwise volatility spillover indices. Our results summarize as follows: (1) The volatility spillover indices present peaks and troughs during some periods, such as shale gas revolution, financial crisis, and oil price crash; (2) After the U.S. shale gas revolution, the size of volatility spillover from natural gas future market has reduced sharply, but volatility doesn't decouple from the other three oil future markets; (3) The directional spillover is asymmetric. The crude oil and heating oil futures market are main net transmitter of volatility risk information, while the gasoline and natural gas futures markets are the net receiver; (4) For natural gas future market, the pairwise volatility spillover from crude oil future market has the most significant influence.

The information content of uncertainty indices for natural gas futures volatility forecasting

AbstractWe investigate the information content of five uncertainty indices for the US natural gas futures volatility forecasting. Our investigation is based on the GARCH‐MIDAS framework. The in‐sample outcomes suggest that most of uncertainty indices have a crucial effect on natural gas futures volatility. And the out‐of‐sample prediction results indicate that the geopolitical risk (GPR) and equity market volatility (EMV) indices contain more useful information for natural gas futures volatility. Moreover, according to the empirical results of special periods, we observe that the EMV index exhibits superior predictive ability under the periods of postcrisis, expansions, and low volatility. Our results are confirmed by several robustness checks.

The Natural Gas Announcement Day Puzzle

This paper studies natural gas futures returns on EIA storage announcement days.More than 50% of the annual return is earned on these days. We find a significant difference between announcement and non-announcement day returns, which cannot be explained by the announcement surprise or other control variables. At the intraday level, the return splits half into a pre- and post-announcement part. The pre-announcement return is entirely generated on days when storage levels exceed analysts’ expectations casting doubt on explanations based on informed trading.After transaction and funding cost, a simple trading strategy yields substantial returns.Gas Markets, Announcement Effect, Storage News, Intraday

Spillovers between natural gas, gasoline, oil, and stock markets: Evidence from MENA countries

This paper examines the dependence structure and systemic risk between WTI crude oil futures, New York Harbor gasoline futures, Henry Hub natural gas futures, and important stock markets in the MENA region, paying attention to the periods before and after the mid-2014 oil price crash. Various copula functions along with the Variational Mode Decomposition (VMD) technique and the Conditional Value at Risk (CoVaR) measure are used. The results show evidence of a negative and positive average dependence between energy (crude oil, natural gas, and gasoline) and almost all MENA stock markets before and after the oil crash in the short term. In the long term, we find a significant average dependence before the crash and an upper tail dependence after the crash between crude oil and almost all the MENA stock markets. Natural gas exhibits a positive and upper tail dependence with almost all MENA markets before the crash and an average dependence after the crash. For gasoline, the results however are mixed for the average and tail dependence before and after the crash. Furthermore, the dependence is higher in the long than the short term and after the oil crash than before it. The VaR and CoVaR values show that the expected loss of portfolios is more significant after the crash and in the long term. Finally, the stock markets of the oil-exporting MENA countries are more affected by the energy price shocks than the oil-importing MENA countries as reflected in the intensification of the dependence level and the systemic risk results.

Financial forecasting using stochastic models: reference from multi-commodity exchange of India

<abstract> <p>Non-linear forecasting models, including artificial neural networks, are popularly adopted in financial forecasting. These models require high computational infrastructure and resources for practical training and deployment, unlike linear forecasting models. However, this level of infrastructure and resources is not accessible to most market participants. This paper lays down a systematic approach to build a simplistic and effective forecasting model, which empowers investors to make informed decisions using a minimal computational infrastructure. Natural gas futures traded in the Multi-Commodity Exchange of India were identified as an appropriate asset for the study considering the massive, expected movement in energy consumption pattern in India. We have used data analytics and statistical techniques to identify optimal training strategies and features, which resulted in an accurate linear forecasting model. Data analytics also helped to accurately establish the context of the study with the identification of a positive shift in the sentiments of market participants of the asset, which was duly verified using the substantial change in the valuation of the asset. While formulating the forecasting model, several avenues, including identifying weekly and yearly patterns, introducing seasonality and exogenous variables were explored. The paper concludes that such attempts to reduce external dependency and segregate noise data result into better model performance with minimal computational resource and infrastructure.</p> </abstract>

An Advanced Approach for Improving the Prediction Accuracy of Natural Gas Price

As one of the most important commodity futures, the price forecasting of natural gas futures is of great significance for hedging and risk aversion. This paper mainly focuses on natural gas futures pricing which considers seasonality fluctuations. In order to study this issue, we propose a modified approach called six-factor model, in which the influence of seasonal fluctuations are eliminated in every random factor. Using Monte Carlo method, we first assess and comparative analyze the fitting ability of three-factor model and six-factor model for the out of sample data. It is found that six-factor model has better performance than three-factor model and natural gas futures prices is strongly influenced by winter effect. We then apply the proposed model to predict the price of natural gas futures in the year 2019. It is found that natural gas prices have a weak upward trend in the coming year and are relatively volatile in winter.

Examination of the Spillover Effects among Natural Gas and Wholesale Electricity Markets Using Their Futures with Different Maturities and Spot Prices

This study measures the connectedness of natural gas and electricity spot returns to their futures returns with different maturities. We employ the Henry Hub and the Pennsylvania, New Jersey, and Maryland (PJM) Western Hub Peak as the natural gas price indicator and the wholesale electricity price indicator, respectively. We also use each commodity’s spot prices and 12 types of futures prices with one to twelve months maturities and realize results in fourfold. First, we observe mutual spillover effects between natural gas futures returns and learn that the natural gas futures market is integrated. Second, we observe the spillover effects from natural gas futures returns to natural gas spot returns (however, the same is not evident for natural gas spot returns to natural gas futures returns). We find that futures markets have better natural gas price discovery capabilities than spot markets. Third, we observe the spillover effects from natural gas spot returns to electricity spot returns, and the spillover effects from natural gas futures returns to electricity futures returns. We learn that the marginal cost of power generation (natural gas prices) is passed through to electricity prices. Finally, we do not observe any spillover effects amongst electricity futures returns, except for some combinations, and learn that the electricity futures market is not integrated.

The Future of Natural Gas in Power Generation

For the last 12 years, the natural gas industry has been booming, ever since the Shale Revolution took off. It was a while before the industry began to think of itself in supply “abundance” terms—from expecting to import 8 Bcf/day of liquefied natural gas (LNG) by 2030 to expecting to export 14 or so Bcf/day by then—an expectation that looks increasingly conservative in terms of new‐build capacity. The swing between those two numbers, as measurements of the marginal supply‐demand “over‐under,” in essence signaled the United States was adding natural gas supply that will surpass two times the exports of Qatar, the largest LNG exporter in the world. Domestic demand was expected to grow mightily, as abundance‐driven decreases in price led to big gains in market share in both industrial use and electric power generation. Wow, compared with the pre‐2008 predictions of $20 per million Btu prices, increasing imports, and decreased availability, it was as if the sun had come up.

Information Leakage in Energy Derivatives around News Announcements

We examine the behavior of US crude oil and natural gas futures options implied volatility based measures as proxies for information leakage around news announcements between 2007 and 2017. In the five days preceding news releases, we report abnormal changes in the levels of futures options implied volatility spreads and skew. In addition, we report a statistically significant relationship between abnormal announcement date returns and abnormal changes in pre-announcement implied volatility spreads/skew. Our findings indicate that at least some investors are informed about the details of future crude oil and natural gas news. TOPICS:Options, derivatives Key Findings • The study offers a unique examination of information leakage in crude oil and natural gas futures options prior to commodity specific news between 2007 to 2017. • We report abnormal changes in implied volatility spreads and skew in the five days prior to news announcements. • Pre-announcement abnormal options trading activity indicates that some traders have knowledge about the details of upcoming energy news.

The Natural Gas Announcement Day Puzzle

This paper studies natural gas futures returns on EIA storage announcement days. More than 50% of the annual return is earned on these days. We find a significant difference between announcement and non-announcement day returns, which cannot be explained by the announcement surprise or other control variables. At the intraday level, the return splits half into a pre- and post-announcement part. The pre-announcement return is entirely generated on days when storage levels exceed analysts’ expectations casting doubt on explanations based on informed trading. After transaction and funding cost, a simple trading strategy yields substantial returns.

A Test of Using Markov-Switching GARCH Models in Oil and Natural Gas Trading

In this paper, we test the use of Markov-switching (MS) GARCH (MSGARCH) models for trading either oil or natural gas futures. Using weekly data from 7 January 1994 to 31 May 2019, we tested the next trading rule: to invest in the simulated commodity if the investor expects to be in the low-volatility regime at t + 1 or to otherwise hold the risk-free asset. Assumptions for our simulations included the following: (1) we assumed that the investors trade in a homogeneous (Gaussian or t-Student) two regime context and (2) the investor used a time-fixed, ARCH, or GARCH variance in each regime. Our results suggest that the use of the MS Gaussian model, with time-fixed variance, leads to the best performance in the oil market. For the case of natural gas, we found no benefit of using our trading rule against a buy-and-hold strategy in the three-month U.S. Treasury bills.

Dueling metaphors, fueling futures: “Bridge fuel” visions of coal and natural gas in the United States

Since at least the 1970s, the idea that certain fossil fuels represent transitional fuels on the way to a lower-carbon energy future has persisted in energy discourse. In particular, natural gas has come to occupy a new identity in this discourse, shifting from what was once a negligible portion of the energy mix in the United States to one of the nation's most important energy resources. Though the “bridge fuel” metaphor was once fairly unambiguous in its meaning, the term has evolved over time to incorporate many visions of the future roles of coal and natural gas in the energy mix. To probe the variety of these meanings in public discourse, we assess the bridge fuel narrative in news articles and government publications as a means to understand the complexity and context of political debates over the unfolding future of natural gas. Using a qualitative coding scheme, we examine five different interpretations of the bridge fuel metaphor between 1988 and 2016. We argue that with its unique quality of facilitating both consensus-building and contestation, the bridge fuel metaphor's simplicity and accessibility belie its power to reveal changing expectations, values, and visions of the future of American energy.

Modelling and Prediction of Financial Trading Networks: An Application to the New York Mercantile Exchange Natural Gas Futures Market

SummaryOver recent years there has been a growing interest in using financial trading networks to understand the microstructure of financial markets. Most of the methodologies that have been developed so far for this have been based on the study of descriptive summaries of the networks such as the average node degree and the clustering coefficient. In contrast, this paper develops novel statistical methods for modelling sequences of financial trading networks. Our approach uses a stochastic block model to describe the structure of the network during each period, and then links multiple time periods by using a hidden Markov model. This structure enables us to identify events that affect the structure of the market and make accurate short-term prediction of future transactions. The methodology is illustrated by using data from the New York Mercantile Exchange natural gas futures market from January 2005 to December 2008.

Extracting Signals from High-Frequency Trading with Digital Signal Processing Tools

As algorithms replace a growing number of tasks performed by humans in the markets, there have been growing concerns about an increased likelihood of cascading events, similar to the Flash Crash of May 6, 2010. To address these concerns, researchers have employed a number of scientific data analysis tools to monitor the risk of such cascading events. As an example, the authors of this article investigate the natural gas (NG) futures market in the frequency domain and the interaction between weather forecasts and NG price data. They observe that Fourier components with high frequencies have become more prominent in recent years and are much stronger than could be expected from an analytical model of the market. Additionally, a significant amount of trading activity occurs in the first few seconds of every minute, which is a tell-tale sign of time-based algorithmic trading. To illustrate the potential of cascading events, the authors further study how weather forecasts drive NG prices and show that, after separating the time series by season to account for the different mechanisms that relate temperature to NG price, the temperature forecast is indeed cointegrated with NG price. They also show that the variations in temperature forecasts contribute to a significant percentage of the average daily price fluctuations, which confirms the possibility that a forecast error could significantly affect the price of NG futures. TOPICS:Statistical methods, simulations, big data/machine learning Key Findings • High-frequency components in the trading data are stronger than expected from a model assuming uniform trading during market hours. • The dominance of the high-frequency components have been increasing over the years. • Relatively small changes in temperature could create a large price fluctuation in natural gas futures contracts.