Extreme Weather Forecasts Impact on U.S. Natural Gas Futures Prices


Book Description

We test the efficiency of the U.S. Natural Gas futures market against both observed and forecasted weather information from across the U.S., and show that the NG futures market incorporates complex meteorological information. We show that U.S. Natural Gas futures are affected by forecasted temperatures, but not by observed temperatures, except for one specific case.We then elaborate the expectation that the risk linked to the uncertainty intrinsic to weather forecasts will be rewarded by market forces. We identify a novel and material “extreme weather forecast” risk premium which outperforms the S&P500 on both an absolute and a risk adjusted basis. Our results, which have never previously been described in literature, are consistent throughout the thirty-year period from the U.S. Natural Gas market deregulation of 1990 up to 2019.




Volatility in the Natural Gas Market


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Comparing Price Forecast Accuracy of Natural Gas Models AndFutures Markets


Book Description

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.




A Statistical Analysis of the Natural Gas Futures Market


Book Description

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.




Extreme Weather and The Financial Markets


Book Description

The positive effects of climate change on the market Record-setting snowfall, cyclones in Australia, chronic drought in Russia, and other dramatic weather events are getting increased attention from scientists and the general public. The effects of climate change present challenges to many sectors, but also present major investing opportunities in the stock, bond, and futures markets. Extreme Weather and The Financial Markets looks at climate change from an investor's standpoint. The climate change debate is somewhat irrelevant to those in the financial industry, since we already live with more than enough extreme climate events to impact the financial markets. To the extent that environmental scientists are correct and global climate change is real and getting worse, the more investment opportunities we have The book presents investment ideas that will work under today's global climate condition and will become even more lucrative if global climate change continues Written by Larry Oxley, an acclaimed author who has personally outperformed the index in the Basic Materials sectors (i.e. chemicals, metals, mining, and forest products) for each of the last five years—in both good and bad markets and throughout the global recession—in a portfolio of nearly $2 billion Focusing on the investment opportunities during dramatic weather events, Extreme Weather and The Financial Markets offers advice on how to capitalize on global climate change.




Natural Gas Prices Forecast Comparison--AEO Vs. Natural Gas Markets


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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.




Assessment of the Possibility of Forecasting Future Natural Gas Curtailments


Book Description

This study provides a preliminary assessment of the potential for determining probabilities of future natural-gas-supply interruptions by combining long-range weather forecasts and natural-gas supply/demand projections. An illustrative example which measures the probability of occurrence of heating-season natural-gas curtailments for industrial users in the southeastern US is analyzed. Based on the information on existing long-range weather forecasting techniques and natural gas supply/demand projections enumerated above, especially the high uncertainties involved in weather forecasting and the unavailability of adequate, reliable natural-gas projections that take account of seasonal weather variations and uncertainties in the nation's energy-economic system, it must be concluded that there is little possibility, at the present time, of combining the two to yield useful, believable probabilities of heating-season gas curtailments in a form useful for corporate and government decision makers and planners. Possible remedial actions are suggested that might render such data more useful for the desired purpose in the future. The task may simply require the adequate incorporation of uncertainty and seasonal weather trends into modeling systems and the courage to report projected data, so that realistic natural gas supply/demand scenarios and the probabilities of their occurrence will be available to decision makers during a time when such information is greatly needed.