Natural Gas Price Volatility Research Articles

Exploring the influence of the geopolitical risks on the natural resource price volatility and correlation: Evidence from DCC-MIDAS-X model

This paper uses the expanded dynamic conditional correlation mixed data sampling (DCC-MIDAS-X) model to examine the impact of low-frequency geopolitical risks (GPR) on high-frequency volatility and the correlation of natural resource prices. The empirical results show that GPR and its sub-indices can significantly affect the volatility and correlation of natural resources. The increase in Geopolitical Threats (GPRT) significantly affects the long-run volatility of European Union Allowance (EUA) and Brent crude oil prices. Geopolitical Acts (GPRA) mainly affect the long-run volatility of natural gas prices. In addition, the long-run correlation coefficient, long-run correlation coefficient indicates a competitive relationship between natural resources. Finally, increased GPR and GPRT can significantly reduce the long-run correlation between natural resources. Based on its conclusions, this research makes policy suggestions for businesses and the government. Investors should effectively identify different types of geopolitical risks and their influences to reduce losses and optimize investments. Governments could adjust their policies appropriately to avoid economic shocks due to natural resource volatility caused by geopolitical risks and ensure a stable supply of natural resources.

The role of natural gas in setting electricity prices in Europe

The EU energy and climate policy revolves around enhancing energy security and affordability, while reducing the environmental impacts of energy use. The European energy transition has been at the centre of debate following the post-pandemic surge in power prices in 2021 and the energy crisis following the 2022 Russia-Ukraine war. Understanding the extent to which electricity prices depend on fossil fuel prices (specifically natural gas) is key to guiding the future of energy policy in Europe. To this end, we quantify the role of fossil-fuelled vs. low-carbon electricity generation in setting wholesale electricity prices in each EU-27 country plus Great Britain (GB) and Norway during 2015-2021. We apply econometric analysis and use sub/hourly power system data to estimate the marginal share of each electricity generation type. The results show that fossil fuel-based power plants set electricity prices in Europe at approximately 58% of the time (natural gas 39%) while generating only 34% of electricity (natural gas 18%) a year. The energy transition has made natural gas the main electricity price setter in Europe, with gas determining electricity prices for more than 80% of the hours in 2021 in several countries such as Belgium, GB, Greece, Italy, and the Netherlands. Hence, Europe’s electricity markets are highly exposed to the geopolitical risk of gas supply and natural gas price volatility, and the economic risk of currency exchange.

Role of weather in the natural gas market: Insights from the STL‐GARCH‐W method

AbstractWeather has been shown to affect natural gas markets, but there is limited research on the strength and manner in which weather affects predictions of natural gas volatility. In this study, six weather indicators are used as exogenous variables, and seasonal‐trend decomposition‐generalized autoregressive conditional heteroskedasticity‐Weather (STL‐GARCH‐W) and STL‐GJR‐GARCH‐W models are constructed to explore the effect of weather on global natural gas market. The empirical findings indicate that temperature and precipitation have a notable positive effect on natural gas, while solar radiation has a prominent negative effect. Furthermore, the STL‐GARCH‐W model outperform the STL‐GJR‐GARCH‐W model and the benchmark STL‐GARCH model when temperature, precipitation, and solar radiation are considered. In addition, the January effect has been shown to significantly influence natural gas price volatility. Finally, most parameters in both models are of statistical significance, demonstrating that both models accurately forecast natural gas volatility and emphasizing the importance of weather indicators for modelling natural gas price volatility. Our study provides new insights for energy market investors and policy makers.

Liberalization and the volatility of gas prices: Exploring their relation in times of abundance and scarcity

Agencies and analysts attribute the current volatility of natural gas prices to a temporary convergence of exogenous shocks such as the post-COVID-19 economic recovery and the conflict in Ukraine. This paper hypothesizes that long-term structural and policy factors endogenous to the energy sector also play a relevant role. Through a case study of three major gas markets – UK, US, and EU – the paper finds that countries characterized by abundant domestic production are less exposed to price volatility as they benefit from lower market uncertainty and asset specificity. This encourages energy firms to outsource production and rely on more flexible and shorter-term contracts. A structural condition of abundance supported by liberalization policies and a developed infrastructure network allows energy firms to easily switch suppliers or buyers without being exposed to the opportunistic behavior of the commercial counterparty, increasing market competition and reducing prices. By contrast, import-dependent countries face greater uncertainty and asset specificity, as supplies and prices are not guaranteed and they heavily depend from trends in international markets. In this context, the contractual and structural flexibility brought about by liberalization policies may expose to further insecurity of supply and volatility of prices, as exporters are contractually disengaged from commitments on volumes and prices. In periods of international scarcity, they may redirect supplies to importers that offer a premium in price. This suggests that import-dependent countries could reduce their exposure to price volatility (i) by increasing domestic production (ii) while envisaging the coexistence of contractual models based on both market competition and vertical integration, to take advantage from low spot prices in periods of international abundance and contain the surge in periods of scarcity.

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.

Transition risk, physical risk, and the realized volatility of oil and natural gas prices

The climate change-related risks have been classified into two namely physical risk and transition risk based on the losses suffered and the response to mitigate and adapt to the paradigm shift in environmental sustainability. Consequently, we test how much predictive value can be exploited from these components of climate risks if they are observed in the pricing of energy commodities that are known to be among the largest emitters of emissions. We note three findings in our empirical analyses in this regard. First, we establish that climate risk influences the volatility of crude oil and natural gas markets and improves their out-of-sample forecasts. Second, the transition climate risk more accurately predicts the out-of-sample forecasts of energy market volatility than the physical climate risk suggesting that investors respond more to the various policies/programmes/initiatives aimed at mitigating the climate change-related losses and fatalities and adapting to the paradigm shift in environmental sustainability than paying attention to the physical damages associated with climate change. Finally, investors are more likely to derive higher economic gains when climate risk is observed than when it is not and the outcome from transition risk is greater than that of physical risk. We further highlight some implications of our findings for investors and policy makers.

Environmental Finance: An Interdisciplinary Review

• We provide a systematic review of environmental finance. • Environmental finance research has experienced significant growth in recent years. • Current main research streams are identified and reviewed. • Emerging research themes are proposed and analyzed. • We provide an interdisciplinary research agenda for environmental finance. Environmental finance has gained considerable attention globally as an emerging interdisciplinary research area. This study uses bibliometric analysis to systematically review major studies on environmental finance-related areas published since the 1970s. Through a bibliometric analysis of 892 environmental finance-related articles sourced from the Web of Science database, we identified the main research streams and illustrated the trending research themes of environmental finance. We find that publications related to environmental finance have increased exponentially over the past decade. Current research streams include corporate and social responsibility (CSR), climate negotiations, natural gas price volatility, national policy, and cost comparisons. Further analysis of the recent five years of literature shows that emerging research topics include climate finance, sustainable finance, firm value, climate risk, and green bonds. Finally, we conclude with a future research agenda for environmental finance.

Transition Risk, Physical Risk, and the Realized Volatility of Oil and Natural Gas Prices

Transition Risk, Physical Risk, and the Realized Volatility of Oil and Natural Gas Prices

Transition Risk, Physical Risk, and the Realized Volatility of Oil and Natural Gas Prices

Transition Risk, Physical Risk, and the Realized Volatility of Oil and Natural Gas Prices

Kicking Ash: Who (or What) is Winning the “War on Coal”?

US coal‐fired generating capacity has shed nearly 25% of its footprint between 2011 and 2018. Multiple factors—regulation, natural gas prices, renewable energy adoption, and environmental activism—have each been lauded by various stakeholders for this trend. To improve our understanding of this environmental technology transition, it is important to determine the extent to which each of these factors has accelerated coal unit retirements. We do so in this study through an accelerated failure time model that utilizes data on US coal‐fired generating units from 2008 through 2016. Our findings indicate that environmental regulation, reduced natural gas price volatility, increased utilization of renewable capacity, and the Sierra Club’s Beyond Coal campaign have all accelerated coal unit retirements. We do not, however, find evidence that natural gas price levels or the penetration of renewable capacity have accelerated coal unit retirements. Among the significant drivers, we estimate that the Beyond Coal campaign has had the greatest effect per unit, reducing expected operating life by an average of 24.4 months. However, among these factors, federal regulation affected the greatest number of coal units, and thus had the greatest aggregate impact on coal retirements. In post hoc analysis, we estimate that 40% of the generation that had been provided by retired coal‐fired units spilled over to surviving coal‐fired units. However, we estimate the 60% that was displaced from the coal fleet resulted in a 103.6 to 178.2 million metric ton net reduction in [Formula: see text] emissions each year.

What drives volatility of the U.S. oil and gas firms?

We study how the day-ahead stock price volatility of 15 firms that are S&P 500 constituents in the Oil & Gas Exploration & Production sub-industry is driven through six volatility factors represented by realized volatilities, namely, (i) firms’ own volatility, (ii) industry market volatility, (iii) local (U.S.) market volatility, (iv) world equity market volatility, (v) oil price volatility, and (vi) natural gas price volatility. Existing studies have reported results based on analysis of one or few volatility components, but given the high dependence among volatility factors, this might bias (overestimate) the true importance of each of the volatility factors on the price fluctuation of stocks in the Oil & Gas Exploration & Production sub-industry. To take into account this inter-relatedness of volatility factors, we study all volatility factors together. Using augmented heterogeneous autoregressive (HAR) models and dynamic model averaging, our analysis shows that market volatility is most influential, followed by a stock’s own volatility and industry level volatility. The role of the volatility of the oil market is of lesser importance, while the volatility of the world equity market does not appear to contain incremental information useful for predicting the volatility of firms in the Oil & Gas Exploration & Production sub-industry. The role of the natural gas market is specific. An in-sample analysis suggests a negative relationship between firm-level volatility and volatility on the natural gas market. However, in an out-of-sample framework, the volatility of the natural gas market appears to be unrelated to firm-level volatility. Dynamic model averaging further suggests that the market and industry factors are time-varying. These findings have implications for financial risk management, as we show that in an out-of-sample framework, HAR models augmented with volatility factors outperform the plain HAR model by up to a 3.88% increase in volatility forecast accuracy.

The volatility of natural gas prices - Structural shocks and influencing factors

The paper examines the primary drivers and factors influencing the volatility of natural gas prices in the world from January 2007 to July 2020. In addition to the narrow dependence between crude oil and natural gas prices, the influence of renewable energy production and coal production on the price of natural gas has been studied. For that purpose, the method of multiple linear regression was used. The results show that the volatility of natural gas prices significantly depends on the type of the shock in the natural gas market, and that the total production of energy from renewable sources, production of coal and natural gas and the price of crude oil have a significant impact on the price of gas.

What drives volatility in natural gas prices?

We examine the primary drivers of U.S. natural gas price volatility. To do so, we apply a structural heterogeneous autoregressive VAR (SHVAR) model, accommodating structural breaks in both the coefficient and volatility, to monthly time series data on natural gas supply, demand and price between January 1978 and July 2018. We detect several structural changes in coefficients and shock volatility in the natural gas market. Our findings indicate that the response of natural gas prices differs significantly depending on the regime and type of shock in the natural gas market. While demand shocks specific to the natural gas market are the primary drivers of natural gas price volatility, structural supply shocks also play a significant role in explaining movements in natural gas prices. Our results suggest that failure to consider structural breaks in the coefficient and volatility may result in biased estimates and distorted impulse responses of the impact of shocks on natural gas price volatility.

Parameter variation and the components of natural gas price volatility

Estimating a static coefficient for a deseasoned gas storage or weather variable implicitly assumes that market participants react identically throughout the year (and over each year) to that variable. In this analysis we model natural gas returns as a linear function of gas storage and weather variables, and we allow the coefficients of this function to vary continuously over time. This formulation takes into account that market participants continuously try to improve their forecasts of market prices, and this likely means they continuously change the scale of their reaction to changes in underlying variables. We use this model to also calculate conditional natural gas volatility and the proportion of volatility attributable to each factor. We find that return volatility is higher in the winter, and this increase is attributable to increases in the proportion of volatility due to weather and natural gas storage. We provide time series estimates of the changing proportion of volatility attributable to each factor, which is useful for hedging and derivatives trading in natural gas markets. ∗Associate Professor of Finance, Clarion University of Pennsylvania, 840 Wood St. Clarion PA 16214. matt@complete-markets.com

Investment in electricity capacity under fuel cost uncertainty: Dual-fuel and a mix of single-fuel technologies

We study the effect of the price and price volatility of natural gas on investment in electricity capacity in two technology scenarios: (1) dual-fuel units that use natural gas and diesel; and (2) a mix of single-fuel plants that use coal or natural gas. We develop a two-stage (capacity and operation) model and derive analytical solutions for both scenarios. Based on the observed log-normal distribution of the natural gas price, we show that optimal capacity investment increases moderately with natural gas price volatility, countering a commonly held view that fuel cost uncertainty tends to discourage capacity investment. Thus, higher volatility of the natural gas price tends to reduce the 'missing money' problem. We use Texas data to show that higher gas price volatility implies higher profits and consumer surplus in the first scenario, even when the per MWh diesel cost is much higher than the expected value of the per MWh gas cost. In the second scenario, firms invest only in gas capacity, unless the per MWh coal cost is significantly below the expected per MWh gas cost, thus explaining the popularity of gas generation.

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

This study investigates the impacts of driving factors for natural gas prices during the period from 1999 to 2017. A data-driven approach, namely the directed acyclic graph (DAG), is first employed to disclose the contemporaneous relations among natural gas, crude oil and various factors. The main results indicate that there is a stable contemporaneous causal flow from crude oil to natural gas. Unlike most of the previous research, we found a long-term equilibrium relationship between crude oil and natural gas returns when additional factors were taken into account. However, the impact of oil price returns on natural gas price volatility has decreased after the 2008 global financial crisis. Finally, storage and seasonality factors can never be ignored when analysing natural gas prices, while the impact of speculative activity on natural gas volatility is relatively weak.

Google search keywords that best predict energy price volatility

Internet search activity data has been widely used as an instrument to approximate trader attention in different markets. This method has proven effective in predicting market indices in the short-term. However, little attention has been paid to demonstrating search activity for keywords that best grab investor attention in different markets. This study attempts to build the best practically possible proxy for attention in the market for energy commodities using Google search data. Specifically, we confirm the utility of Google search activity for energy related keywords are significant predictors of volatility by showing they have incremental predictive power beyond the conventional GARCH models in predicting volatility for energy commodities' prices. Starting with a set of ninety terms used in the energy sector, the study uses a multistage filtering process to create combinations of keywords that best predict the volatility of crude oil (Brent and West Texas Intermediate), conventional gasoline (New York Harbor and US Gulf Coast), heating oil (New York Harbor), and natural gas prices. For each commodity, combinations that enhance GARCH most effectively are established as proxies of attention. The results indicate investor attention is widely reflected in Internet search activities and demonstrate search data for what keywords best reveal the direction of concern and attention in energy markets.

The growing importance of natural gas as a predictor for retail electricity prices in US

In the literature, there are tests about long and or short Granger causality between primary energy sources such as natural gas and secondary energy sources like electricity. Nevertheless the existence of a causal relationship or not, cannot clearly illustrate the dynamics in their relationship over time. Towards this direction, we apply a one step ahead rolling forecast and examine the performance of the average cost of natural gas as a predictor for retail electricity prices at national and regional level over time. Our analysis answers if, how and when the cost of natural gas becomes a significant predictor of electricity prices. Besides lower natural gas prices, the existence of sufficient gas infrastructures or a competitive market environment or both of them is needed in order to couple retail electricity prices with the cost of natural gas. This growing importance must be taken into account from policy makers by allowing additional gas based stations in the main-grid but at the same time avoiding the risks from the natural gas price volatility.

The Most Predictive Energy Search Terms

Internet search activity data has been widely used as an instrument to approximate trader attention in different markets. This method has proven effective in predicting market indices in the short-term. However, little attention has been paid to comparing various search keywords and finding the most effective terms representing attention in different markets. This study attempts to build the best practically possible proxy for attention in the market for major energy commodities using Google search data. Specifically, first we confirm that Google search activity for energy-related keywords are significant predictors of energy price volatility. We show that search trends data have incremental predictive power beyond the conventional GARCH models. Next, starting with a set of ninety terms used in the energy sector, the study uses a multistage filtering process to create combinations of keywords that best predict the volatility of crude oil (Brent and West Texas Intermediate), conventional gasoline (New York Harbor and US Gulf Coast), Heating Oil (New York Harbor), and natural gas prices. For each commodity, combinations that enhance GARCH most effectively are established as proxies of attention. The results indicate investor attention is widely reflected in internet search activities. The results also demonstrate search data for what keywords best reveal the direction of concern and attention in energy markets.

Supply and demand determinants of natural gas price volatility in the U.K.: A vector autoregression approach

Since 2008, the U.K. natural gas market has witnessed a marked drop in volatility. This fall has coincided with specific events in oil and gas sector such as the onset of the U.S. “shale gas revolution” and the subsequent rerouting of liquefied natural gas (LNG) shipments from the U.S. to other markets such as Asia and Europe. LNG cargoes, along with other sources of flexibility such as underground storages and interconnector import, can potentially reduce volatility. On the other hand, demand shocks can increase volatility. To examine the dynamics relationship between daily shocks in U.K. gas demand and supply, and the gas spot price volatility, we use a vector autoregressive (VAR) model. While we find evidence that daily deviations in aggregated gas demand significantly impacts volatility, we are unable to find direct evidence for an impact from shocks in disaggregated demand or supply. In fact, one important contribution of the paper is to suggest that flexible sources of supply such as LNG, storage and interconnector flows react to shocks in retail demand, dampening their potential effects on volatility.