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This paper attempts to understand the price dynamics of the North American natural gas market through a statistical survey that includes an analysis of the variables influencing the price and volatility of this energy market. The analysis develops a theoretical model for the conditional reactions to weekly natural gas inventory reports, and develops an extended theory of errors in natural gas inventory estimates. The central objective of this thesis is to answer the fundamental question of whether the volatility of natural gas futures are conditional on the season or the level of the natural gas in inventory and how accurate are analysts at forecasting the inventory level. Commodity prices are volatile, and volatility itself varies over time. I examine the role of volatility in shortrun natural gas market dynamics and the determinants of error in inventory estimates leading to this variance. I develop a structural model that equates the conditional volatility response to the error made in analyst forecasts, inherently relating analyst sentiment to volatility and price discovery. I find that in the extremes of the inventory cycle (i.e., near peak injection/withdraw) that variance is particularly strong, and significantly higher than non-announcement days. The high announcement day volatility reflects larger price changes. With statistical significance, we can conclude that when the natural gas market is under-supplied, the near-term Henry Hub Natural Gas futures contract becomes nearly twice as volatile than in an oversupplied market. Furthemore, analysts are more prone to make errors in their estimates of weekly inventory levels around these same time periods. Natural gas is an essential natural resource and is used in myriad aspects of the global economy and society. As we look to develop more sustainable energy policies, North America's abundant clean-burning natural gas will hold an essential role in helping us to secure our future energy independence. An ability to understand the factors influencing it is supply and demand, and thus price, are and will continue to be essential.
This paper analyzes the integration of the American, European, and Asian natural gas markets over the period 2016-2022, with a focus on how the demand shock caused by the COVID-19 pandemic and the supply shock caused by geopolitical tensions in the European market affected this integration. We also examine which regional market is leading in reflecting new information and shocks into the market price. Our analysis indicates that the market integration process has been impacted by external shocks, leading to a decrease in the degree of integration between the European and Asian markets. Additionally, we find that the American market is no longer integrated with the other two markets after the supply shock, potentially due to the US's congested and fully utilized LNG infrastructure. Our analysis also shows that the gas price differentials adjust asymmetrically in response to disturbances, suggesting that markets respond differently to positive and negative shocks. Moreover, we show that the lead/lag relationship changes over time and exhibits a dynamic behavior. Finally, we discuss the fundamental changes in the global gas market that align with our empirical results.
Natural gas pricing should be as critically important to the general public as it is to industry specialists. Pricing is the basis of balancing the interests of European and Asian consumers of power and electricity with those of the limited number of potential suppliers of natural gas. Given that natural gas is a foundational transition fuel source that will not be supplanted by renewals for many, many years, the consequences of market failure from incorrect pricing mechanisms could result in the industry missing the new investment cycle. In addressing the critical balancing role of natural gas pricing, ‘Foundations of Natural Gas Price Formation’ presents an in-depth analysis of the fundamentals of natural gas price formation and outlines the distinctive characteristics of natural gas that make it a unique commodity by examining the specific factors underpinning gas pricing that result in a hybrid pricing system special to natural gas. The book argues that the patrons of spot pricing through gas hubs are promoting an incorrect understanding of gas markets that will lead to market failure and to potential critical supply shortages in the near future. ‘Foundations of Natural Gas Price Formation’ defends the system of oil-indexed pricing as an accurate, market-based mechanism that has stood the test of time.
Commodities have become an important component of many investors' portfolios and the focus of much political controversy over the past decade. This book utilizes structural models to provide a better understanding of how commodities' prices behave and what drives them. It exploits differences across commodities and examines a variety of predictions of the models to identify where they work and where they fail. The findings of the analysis are useful to scholars, traders and policy makers who want to better understand often puzzling - and extreme - movements in the prices of commodities from aluminium to oil to soybeans to zinc.
Since its launch in 2001, Gas Trading Manual (GTM) has established itself as the leading information source on the international gas market. Compiled from the contributions of some of the most senior and widely respected figures in the trade, this edition provides detailed and accurate analysis on all aspects of this complex business from the geography of gas through to the markets, trading instruments, contracts, gas pricing, accounting and taxation. This edition further enhances its reputation as the indispensable practical companion for all those involved in the trading of gas.
"This paper examines the relationship between spot and futures prices for energy commodities (crude oil, gasoline, heating oil markets and natural gas). In particular, we examine whether futures prices are (1) an unbiased and/or (2) accurate predictor of subsequent spot prices. We find that while futures prices are unbiased predictors of future spot prices, with the exception those in the natural gas markets at the 3-month horizon. Futures do not appear to well predict subsequent movements in energy commodity prices, although they slightly outperform time series models"--National Bureau of Economic Research web site.
We investigate the impact of Thompson Reuters News Analytics (TRNA) news sentiment on the price dynamics of natural gas futures traded on the New York Mercantile Exchange (NYMEX). We propose a Local News Sentiment Level (LNSL) model, based on the Local Level model of Durbin and Koopman (2001), to construct a running series of news sentiment on the basis of the 5-minute time grid. Additionally, we construct several return and variation measures to proxy for the fine dynamics of the front month natural gas futures prices. We employ event studies and Granger causality tests to assess the effect of news on the returns, price jumps and the volatility.We find significant relationships between news sentiment and the dynamic characteristics of natural gas futures returns. For example, we find that the arrival of news in non-trading periods causes overnight returns, that news sentiment is Granger caused by volatility and that strength of news sentiment is more sensitive to negative than to positive jumps. In addition to that, we find strong evidence that news sentiment severely Granger causes jumps and conclude that market participants trade as some function of aggregated news.We apply several state-of-the-art volatility models augumented with news sentiment and conduct an out-of-sample volatility forecasting study. The first class of models is the generalized autoregressive conditional heteroskedasticity models (GARCH) of Engle (1982) and Bollerslev (1986) and the second class is the high-frequency-based volatility (HEAVY) models of Shephard and Sheppard (2010) and Noureldin et al. (2011). We adapt both models to account for asymmetric volatility, leverage and time to maturity effects. By augmenting all models with a news sentiment variable, we test the hypothesis whether including news sentiment in volatility models results in superior volatility forecasts. We find significant evidence that this hypothesis holds.