Natural gas prices and the gas storage report: Public news and volatility in energy futures markets

Linn, Scott C.; Zhu, Zhen

doi:10.1002/fut.10115

Cited by 72 publications

(44 citation statements)

References 35 publications

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“…They posit that this phenomenon is because the flow of weather information does not stop over the weekend whereas information flows for equity market are more concentrated during weekdays. The significantly positive STKDAY t coefficient indicates that the release of the weekly natural gas storage report generates considerable volatility and confirms the findings of Murry and Zhu (2004) and Linn and Zhu (2004).…”

Section: Estimation Resultssupporting

confidence: 75%

“…Natural gas futures market is highly liquid with daily trading volumes of 30,000-50,000 contracts for the nearest month and 10,000-30,000 contracts for the second nearest month in recent years (Linn and Zhu, 2004 …”

Section: Natural Gas Marketmentioning

confidence: 99%

“…Examining intraday volatility, Linn and Zhu (2004) show that natural gas price volatility is considerably greater around the time when the natural gas storage report is released.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Weather, storage, and natural gas price dynamics: Fundamentals and volatility

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Energy Economics

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Section: Estimation Resultssupporting

confidence: 75%

Section: Natural Gas Marketmentioning

confidence: 99%

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Weather, storage, and natural gas price dynamics: Fundamentals and volatility

2007

Energy Economics

163

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“…Shawky, Marathe and Barret (2003) use dynamic volatility models to determine the minimum variance hedge ratios for electricity futures. Linn and Zhu (2004) study the effect of natural gas storage report announcements on intraday volatility patterns in gas prices. They also find evidence of strong intraday patterns in natural gas price volatility.…”

Section: Volatility Forecasting In Fields Outside Financementioning

confidence: 99%

Volatility Forecasting

Andersen¹,

Bollerslev²,

Christoffersen³

et al. 2005

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Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management.Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly.

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“…However, the vast majority of the research has focused on financial markets, with the focus only recently turning to the energy markets 2 (Wilson et al, 1996;Yang et al, 2002;Linn and Zhu, 2004;Pindyck, 2004;Kuper and van Soest, 2006;Mohammadi and Su, 2010;Wei et al, 2010;Kang and Yoon, 2013).…”

Section: Introductionmentioning

confidence: 99%

Coupling High-Frequency Data with Nonlinear Models in Multiple-Step-Ahead Forecasting of Energy Markets' Volatility

Baruník

Křehlík

2014

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: Terms of use: Documents inIn the past decade, the popularity of realized measures and various linear models for volatility forecasting has attracted attention in the literature on the price variability of energy markets. However, results that would guide practitioners to a specifc estimator and model when aiming for the best forecasting accuracy are missing. This paper contributes to the ongoing debate with a comprehensive evaluation of multiple-step-ahead volatility forecasts of energy markets using several popular high-frequency measures and forecasting models. To capture the complex patterns hidden to linear models commonly used to forecast realized volatility, this paper also contributes to the literature by coupling realized measures with artificial neural networks as a forecasting tool. Forecasting performance is compared across models as well as realized measures of crude oil, heating oil, and natural gas volatility during three qualitatively distinct periods covering the precrisis period, recent global turmoil of markets in 2008, and the most recent post-crisis period. We conclude that coupling realized measures with arti_cial neural networks results in both statistical and economic gains, reducing the tendency to over-predict volatility uniformly during all tested periods. Our analysis favors the median realized volatility, as it delivers the best performance and is a computationally simple alternative for practitioners.

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Natural gas prices and the gas storage report: Public news and volatility in energy futures markets

Cited by 72 publications

References 35 publications

Weather, storage, and natural gas price dynamics: Fundamentals and volatility

Weather, storage, and natural gas price dynamics: Fundamentals and volatility

Volatility Forecasting

Coupling High-Frequency Data with Nonlinear Models in Multiple-Step-Ahead Forecasting of Energy Markets' Volatility

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