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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 evaluates the accuracy of two methods to forecast natural gas prices: using the Energy Information Administration's ''Annual Energy Outlook'' forecasted price (AEO) and the ''Henry Hub'' compared to U.S. Wellhead futures price. A statistical analysis is performed to determine the relative accuracy of the two measures in the recent past. A statistical analysis suggests that the Henry Hub futures price provides a more accurate average forecast of natural gas prices than the AEO. For example, the Henry Hub futures price underestimated the natural gas price by 35 cents per thousand cubic feet (11.5 percent) between 1996 and 2003 and the AEO underestimated by 71 cents per thousand cubic feet (23.4 percent). Upon closer inspection, a liner regression analysis reveals that two distinct time periods exist, the period between 1996 to 1999 and the period between 2000 to 2003. For the time period between 1996 to 1999, AEO showed a weak negative correlation (R-square = 0.19) between forecast price by actual U.S. Wellhead natural gas price versus the Henry Hub with a weak positive correlation (R-square = 0.20) between forecasted price and U.S. Wellhead natural gas price. During the time period between 2000 to 2003, AEO shows a moderate positive correlation (R-square = 0.37) between forecasted natural gas price and U.S. Wellhead natural gas price versus the Henry Hub that show a moderate positive correlation (R-square = 0.36) between forecast price and U.S. Wellhead natural gas price. These results suggest that agencies forecasting natural gas prices should consider incorporating the Henry Hub natural gas futures price into their forecasting models along with the AEO forecast. Our analysis is very preliminary and is based on a very small data set. Naturally the results of the analysis may change, as more data is made available.
The purpose of this article is to compare the accuracy of forecasts for natural gas prices as reported by the Energy Information Administration's Short-Term Energy Outlook (STEO) and the futures market for the period from 1998 to 2003. The analysis tabulates the existing data and develops a statistical comparison of the error between STEO and U.S. wellhead natural gas prices and between Henry Hub and U.S. wellhead spot prices. The results indicate that, on average, Henry Hub is a better predictor of natural gas prices with an average error of 0.23 and a standard deviation of 1.22 than STEO with an average error of -0.52 and a standard deviation of 1.36. This analysis suggests that as the futures market continues to report longer forward prices (currently out to five years), it may be of interest to economic modelers to compare the accuracy of their models to the futures market. The authors would especially like to thank Doug Hale of the Energy Information Administration for supporting and reviewing this work.
High Frequency Trading is pervasive across all electronic financial markets. As algorithms replace an increasing number of tasks previously performed by humans, cascading effects similar to the Flash Crash of May 6th 2010 become more likely. In this study, we bring together a number of different data analysis tools to improve our understanding of natural gas futures trading activities. We focus on Fourier analysis and cointegration between weather forecasts and natural gas prices. From the Fourier analysis of Natural Gas futures market, we see strong evidences of High Frequency Trading in the market. The Fourier components corresponding to high frequencies (1) are becoming more prominent in the recent years and (2) are much stronger than could be expected from the overall trading records. Additionally, significant amount of trading activities occur in the first second of every minute, which is a telltale sign of the Time-Weighted Average Price (TWAP) execution algorithms. To illustrate the potential for cascading events, we study how weather forecasts drive natural gas prices. After separating the data according to seasons, the temperature forecast is strongly cointegrated with natural gas price. This splitting of data is necessary because in different seasons the natural gas demand depends on temperature through different mechanisms. We are also able to show that the variations in temperature forecasts contribute to a significant percentage of the average daily price fluctuations, which supports the hypothesis that the variations in temperature dominates the volatility of natural gas trading.
Bringing together leading-edge research and innovative energy markets econometrics, this book collects the author's most important recent contributions in energy economics. In particular, the book:• applies recent advances in the field of applied econometrics to investigate a number of issues regarding energy markets, including the theory of storage and the efficient markets hypothesis• presents the basic stylized facts on energy price movements using correlation analysis, causality tests, integration theory, cointegration theory, as well as recently developed procedures for testing for shared and codependent cycles• uses recent advances in the financial econometrics literature to model time-varying returns and volatility in energy prices and to test for causal relationships between energy prices and their volatilities• explores the functioning of electricity markets and applies conventional models of time series analysis to investigate a number of issues regarding wholesale power prices in the western North American markets• applies tools from statistics and dynamical systems theory to test for nonlinear dynamics and deterministic chaos in a number of North American hydrocarbon markets (those of ethane, propane, normal butane, iso-butane, naptha, crude oil, and natural gas)
The markets for electricity, gas and temperature have distinctive features, which provide the focus for countless studies. For instance, electricity and gas prices may soar several magnitudes above their normal levels within a short time due to imbalances in supply and demand, yielding what is known as spikes in the spot prices. The markets are also largely influenced by seasons, since power demand for heating and cooling varies over the year. The incompleteness of the markets, due to nonstorability of electricity and temperature as well as limited storage capacity of gas, makes spot-forward hedging impossible. Moreover, futures contracts are typically settled over a time period rather than at a fixed date. All these aspects of the markets create new challenges when analyzing price dynamics of spot, futures and other derivatives.This book provides a concise and rigorous treatment on the stochastic modeling of energy markets. Ornstein-Uhlenbeck processes are described as the basic modeling tool for spot price dynamics, where innovations are driven by time-inhomogeneous jump processes. Temperature futures are studied based on a continuous higher-order autoregressive model for the temperature dynamics. The theory presented here pays special attention to the seasonality of volatility and the Samuelson effect. Empirical studies using data from electricity, temperature and gas markets are given to link theory to practice.
Since a major source of income for many countries comes from exporting commodities, price discovery and information transmission between commodity futures markets are key issues for continued economic development. Commodities: Fundamental Theory of Futures, Forwards, and Derivatives Pricing, Second Edition covers the fundamental theory of and derivatives pricing for major commodity markets, as well as the interaction between commodity prices, the real economy, and other financial markets. After a thoroughly updated and extensive theoretical and practical introduction, this new edition of the book is divided into five parts – the fifth of which is entirely new material covering cutting-edge developments. Oil Products considers the structural changes in the demand and supply for hedging services that are increasingly determining the price of oil Other Commodities examines markets related to agricultural commodities, including natural gas, wine, soybeans, corn, gold, silver, copper, and other metals Commodity Prices and Financial Markets investigates the contemporary aspects of the financialization of commodities, including stocks, bonds, futures, currency markets, index products, and exchange traded funds Electricity Markets supplies an overview of the current and future modelling of electricity markets Contemporary Topics discuss rough volatility, order book trading, cryptocurrencies, text mining for price dynamics and flash crashes