Fundamental News and the Behavior of Commodity Prices: Price Discovery and Jumps in U.S. Natural Gas Futures and Spot Prices

Chiou-Wei, Song Zan; Linn, Scott C.; Zhu, Zhen

doi:10.2139/ssrn.1108702

Cited by 3 publications

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“…Chiou‐Wei et al () examine the effect of market uncertainty on the response of the natural gas futures prices to the storage surprises. They use the standard deviation of the individual analysts' forecasts of the change in inventory as a proxy for market uncertainty.…”

Section: Resultsmentioning

confidence: 99%

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Noisy Inventory Announcements and Energy Prices

Halova

Kurov

Kucher

2013

Journal of Futures Markets

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This study examines the effect of oil and gas inventory announcements on energy prices. Previous estimates of this effect suffer from bias due to measurement error in inventory surprises. We utilize intraday futures data for three petroleum commodities and natural gas to estimate the price response coefficients using traditional event study regressions and the identification‐through‐censoring (ITC) technique proposed by Rigobon and Sack [Rigobon and Sack (2008). Asset prices and monetary policy (pp. 335–370). Chicago: University of Chicago Press]. The results show that the bias in OLS estimates of the price impact of inventory surprises is quite large. The ITC coefficient estimates are about twice as large as OLS estimates for petroleum commodities and about four times as large as OLS estimates for natural gas. These results imply that energy prices are more strongly influenced by unexpected changes in inventory than shown in previous research. © 2013 Wiley Periodicals, Inc. Jrl Fut Mark 34:911–933, 2014

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Section: Resultsmentioning

confidence: 99%

“…Second, our results are useful for modeling price dynamics of energy commodities. Large jumps in energy prices are common, and are often triggered by inventory announcements (e.g., Chiou‐Wei, Linn, & Zhu, ). Our estimates of the price response coefficients help explain such extreme price changes.…”

Section: Introductionmentioning

confidence: 99%

Noisy Inventory Announcements and Energy Prices

Halova

Kurov

Kucher

2013

Journal of Futures Markets

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“…For example, Roll (1984) examines orange juice futures, Harvey and Huang (1991) examine currency futures, and Ederington and Lee (1993) study the reaction of interest rate and currency futures. Research on price discovery in natural gas futures markets includes Linn and Zhu (2004) and Chiou-Wei, Linn, and Zhu (2007). Linn and Zhu examine natural gas futures volatility around AGA and EIA announcements and find that the announcements are responsible for market volatility at the day of the release.…”

Section: Introductionmentioning

confidence: 99%

Analyst forecasts and price discovery in futures markets: The case of natural gas storage

Simkins

Turac

2009

Journal of Futures Markets

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We investigate analyst forecasts in a unique setting, the natural gas storage market, and study the contribution of analysts in facilitating price discovery in futures markets. Using a high-frequency database of analyst storage forecasts, we show that the market appears to condition expectations regarding a weekly storage 452 Gay, Simkins, and TuracJournal of Futures Markets release on the analyst forecasts and beyond that of various statistical-based models. Further, we find that the market looks through the reported consensus analyst forecast and places differential emphasis on the individual forecasts of analysts according to their prior accuracy. Also, the market appears to place greater emphasis on analysts' long-term accuracy than on their recent accuracy. forecast accuracy. We consider a few alternative weighting schemes and find supporting evidence that the market looks through the consensus forecast and places differential emphasis on the forecasts of analysts according to their prior accuracy. Further, it appears that the market focuses more on analysts' longterm forecast accuracy than their recent accuracy.Our investigation also entails two sidebars that create interesting information environments for analyzing market behavior. First, gas in storage is highly cyclical and involves two calendar periods during which supply and demand fundamentals alternate in importance and thus present differing challenges to analysts. During the "injection season" (April to October), storage typically accumulates because demand is lower during the warmer months and new inventories are being added in advance of the next winter heating season. During the "withdrawal season" (November to March), inventories are drawn down due to high consumption demand in the winter heating season. Demand shocks are mainly weather driven and are larger in magnitude and less predictable than supply shocks, which are typically technology driven. Hence, weekly storage changes are typically greater and more difficult to predict during the withdrawal season. 3 We find that analyst forecasts are indeed less accurate and more dispersed during the demand-driven withdrawal season than during the supply-driven injection season.Second, we consider a major change in responsibility for conducting the weekly survey. The survey was originated in January 1994 by the American Gas Association (AGA)-the industry trade group. In May 2002, the U.S. Department of Energy's "Energy Information Administration" (EIA) assumed responsibility from the AGA. The EIA implemented several changes to the survey procedures with the intention of reducing noise and producing more accurate estimates. We find that analyst accuracy improved and forecast dispersion decreased following the EIA takeover.Though we do not claim that our weighting schemes are optimal, our study adds to the literature on combining forecasts to produce a superior forecast. A seminal paper by Bates and Granger (1969) first suggested and proposed a method to optimally combine forecasts. This study spawne...

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