How do oil producers respond to oil demand shocks?

Güntner, Jochen

doi:10.1016/j.eneco.2014.03.012

Cited by 42 publications

(25 citation statements)

References 40 publications

(81 reference statements)

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“…However, together the independent variables cause WTI crude oil price movement as indicated by the presence of a long-run or equilibrium relationship. Our evidence therefore contradicts literary perception that there is a short-run relationship between U.S. crude oil inventories and WTI crude oil prices put forward by Ye et al2005;Rifflart and Chevillon (2009), Biu (2011), Schmidbauer and Rosch (2012), Cashinet al (2014 and Guntner (2014). …”

Section: Grangers Causality Testcontrasting

confidence: 90%

“…For example, the EIA (2014) established the basic categories of crude oil price drivers to include crude oil inventory builds among others; and there is extensive research on the effect of each of those explanatory variables put forward by the EIA in determining the price of crude oil in the global market. Such works include; Rifflart andChevillon (2009), Biu (2011), Schmidbauer andRosch (2012), Cashinet al (2014) and Guntner (2014).The effects of changes in supply and demand of crude oil on price (Kutasovic 2012;Fattouh 2010); (the effects of economic growth on oil price (Jiménez-Rodríguez and Sánchez 2004;Husain et al 2015; as well as the effects of currency exchange rates, financial market activity and geopolitics among others (EIA 2014;Rentschler, 2013). While all of these research have enabled some understanding, it appears the effects of non-weather related, inventory level swings on crude oil price has not received as much attention of energy economists.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Short Run Effects of U.S. Crude Oil Inventory Levels on Wti Crude Oil Price From 1993 - 2013

Olasojiand¹,

Acquah-Andoh²

2016

EJEF

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The focus of this research was to investigate the short-term influence of U.S. crude oil inventories on WTI crude oil prices from 1993 to 2013. This study is important for policy makers who wish to reduce the persistent and growing price volatility of crude oil and its related products as well as businesses such as airline companies who wish to make annual budgetary sales decisions. Using OLS multiple regression, cointegration, VECM and Ex-post forecast techniques; we provide evidence of an inelastic relationship in which a 1% increase in U.S. crude oil inventories is associated with 0.46% decrease in WTI crude oil prices; however this was only valid for 22% of WTI crude oil price variation. We also find that past data on U.S. crude oil inventories could be used to predict future WTI crude oil prices movement. Contrary to literature, the results of the VECM analysis indicate there is no short-run relationship between both variables over the trajectory.

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Section: Grangers Causality Testcontrasting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Evaluating the Short Run Effects of U.S. Crude Oil Inventory Levels on Wti Crude Oil Price From 1993 - 2013

Olasojiand¹,

Acquah-Andoh²

2016

EJEF

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“…As Giot [5] summarized extremely high levels of implied volatility is a signal of 'buy' entry points for traders who want to take long positions in the underlying index. This result doesn't lend strong support for Giot's result when using OVX and crude oil spot prices since crude oil price is also affected by global demand, supply and inventory of world's leading countries (see [30,31] and [32] etc.) which is different from equity market.…”

Section: Empirical Analysismentioning

confidence: 80%

The Information Content of OVX for Crude Oil Returns Analysis and Risk Measurement: Evidence from the Kalman Filter Model

Chen

2015

Ann. Data. Sci.

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Crude oil volatility index (OVX) is a new index published by Chicago BoardOption Exchange since 2007. In recent years it emerged as an important alternative measure to track and analyze the volatility of future oil prices. In this paper we firstly model and analyze the dynamic relationship between OVX changes and future crude oil price returns with time-varying coefficients, modeled using the Kalman filter, in the regression models. Empirical results show a weak negative relationship between OVX changes and future crude oil price returns movement, and extremely high/low levels of OVX cannot predict future positive/negative returns well. Secondly, this paper explores whether OVX can predict future realized volatility of crude oil price returns . The empirical findings suggest that OVX serves as an unbiased but not an efficient estimate of the future realized volatility and it includes information of the future realized volatility. Finally the incorporation of information of OVX in measuring market risk is analyzed. The empirical result indicates that Kalman filter based model provides the improved performance than the linear regression model in terms of forecasting accuracy for realized volatility prediction and the reliability for VaR estimate. B Kaijian He

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“…Nevertheless, Baumeister and Hamilton (2015) find evidence for a small, positive short-run supply elasticity. Papers analyzing oil extraction elasticities include Griffin (1985); Hogan (1989); Jones (1990); Dahl and Yücel (1991); Ramcharran (2002); and Güntner (2014). Much of this work pre-dates the shale revolution, and even the more recent literature does not distinguish between unconventional and conventional supply.…”

Section: Literaturementioning

confidence: 99%

The Unconventional Oil Supply Boom: Aggregate Price Response from Microdata

Newell¹,

Prest²

2019

The Energy Journal

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We analyze the price responsiveness of U.S. conventional and unconventional oil supply across three key stages of oil production: drilling, completion, and production. Drilling is the most important margin, with price elasticities of 1.3 and 1.6 for conventional and unconventional drilling respectively. Well productivity declines as prices rise, implying smaller net supply elasticities of about 1.1 and 1.2. Despite similar supply elasticities, the price response of unconventional supply is larger in terms of barrels because of much higher production per well (~10x initially). Oil supply simulations show a 13-fold larger supply response due to the shale revolution. The simulations suggest that a price rise from $50 to $80 per barrel induces incremental U.S. production of 0.6MM barrels per day in 6 months, 1.4MM in 1 year, 2.4MM in 2 years, and 4.2MM in 5 years. Nonetheless, the response takes much longer than the 30 to 90 days than typically associated with the role of "swing producer.

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How do oil producers respond to oil demand shocks?

Cited by 42 publications

References 40 publications

Evaluating the Short Run Effects of U.S. Crude Oil Inventory Levels on Wti Crude Oil Price From 1993 - 2013

Evaluating the Short Run Effects of U.S. Crude Oil Inventory Levels on Wti Crude Oil Price From 1993 - 2013

The Information Content of OVX for Crude Oil Returns Analysis and Risk Measurement: Evidence from the Kalman Filter Model

The Unconventional Oil Supply Boom: Aggregate Price Response from Microdata

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