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U.S. shale oil production and WTI prices behaviour

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  • Monge, Manuel
  • Gil-Alana, Luis A.
  • Pérez de Gracia, Fernando

Abstract

The aim of this paper is to relate the shale oil revolution in the United States with WTI oil price behavior. Since the development of the combination of horizontal drilling techniques together with hydraulic fracturing in the 1970s, known as shale oil, oil markets have undergone a significant transformation with the unexpectedly strong rise in the United States production affecting oil prices. The goal of this paper is two-fold: first, we analyze the relationship of total United States crude oil production and WTI crude oil prices by studying its performance in the time-frequency domain applying wavelet tools for its resolution. Using wavelet methodologies, we observe a shift to higher frequencies of the wavelet coherency for the time period 2003–2009 and lower frequencies for the period 2009–2014. The results also indicate that during the period 2003–2009 the U.S. oil production and WTI oil prices time series are in phase; they move together, with total United States oil production leading. During the period 2009–2014 oil production and WTI oil prices time series are out of phase (negatively correlated), suggesting that oil production increases precede a decrease in WTI oil prices. In the second part of the paper and to give greater credibility to the results obtained through the wavelet transform, we analyze the behavior of WTI crude oil before and after the shale oil boom in the United States employing methodologies based on long run dependence. The results indicate that mean reversion takes place only for the data corresponding to the first subsample, ending at 2003. For the second subsample, as well as for the whole sample, lack of mean reversion is detected with orders of integration equal to or higher than 1 in all cases.

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  • Monge, Manuel & Gil-Alana, Luis A. & Pérez de Gracia, Fernando, 2017. "U.S. shale oil production and WTI prices behaviour," Energy, Elsevier, vol. 141(C), pages 12-19.
    • Handle: RePEc:eee:energy:v:141:y:2017:i:c:p:12-19
    DOI: 10.1016/j.energy.2017.09.055
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    as
    1. Luís Aguiar-Conraria & Maria Soares, 2011. "Oil and the macroeconomy: using wavelets to analyze old issues," Empirical Economics, Springer, vol. 40(3), pages 645-655, May.
    2. Granger, Clive W. J. & Hyung, Namwon, 2004. "Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 399-421, June.
    3. Benes, Jaromir & Chauvet, Marcelle & Kamenik, Ondra & Kumhof, Michael & Laxton, Douglas & Mursula, Susanna & Selody, Jack, 2015. "The future of oil: Geology versus technology," International Journal of Forecasting, Elsevier, vol. 31(1), pages 207-221.
    4. Aguiar-Conraria, LuI´s & Joana Soares, Maria, 2011. "Business cycle synchronization and the Euro: A wavelet analysis," Journal of Macroeconomics, Elsevier, vol. 33(3), pages 477-489, September.
    5. John Baffes & M. Ayhan Kose & Franziska Ohnsorge & Marc Stocker, 2015. "The Great Plunge in Oil Prices: Causes, Consequences, and Policy Responses," Policy Research Notes (PRNs) 94725, The World Bank.
    6. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November.
    7. Christiane Baumeister & Lutz Kilian, 2016. "Understanding the Decline in the Price of Oil since June 2014," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 3(1), pages 131-158.
    8. Lutz Kilian, 2017. "The Impact of the Fracking Boom on Arab Oil Producers," The Energy Journal, International Association for Energy Economics, vol. 0(Number 6).
    9. Luis A. Gil‐Alana, 2008. "Fractional integration and structural breaks at unknown periods of time," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(1), pages 163-185, January.
    10. Alquist, Ron & Guénette, Justin-Damien, 2014. "A blessing in disguise: The implications of high global oil prices for the North American market," Energy Policy, Elsevier, vol. 64(C), pages 49-57.
    11. Aguiar-Conraria, Luís & Azevedo, Nuno & Soares, Maria Joana, 2008. "Using wavelets to decompose the time–frequency effects of monetary policy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(12), pages 2863-2878.
    12. Naccache, Théo, 2011. "Oil price cycles and wavelets," Energy Economics, Elsevier, vol. 33(2), pages 338-352, March.
    13. Ramsey, J.B., 2002. "Wavelets in Economics and Finance: Past and Future," Working Papers 02-02, C.V. Starr Center for Applied Economics, New York University.
    14. Luís Aguiar-Conraria & Maria Joana Soares, 2014. "The Continuous Wavelet Transform: Moving Beyond Uni- And Bivariate Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 28(2), pages 344-375, April.
    15. Kyrtsou, Catherine & Malliaris, Anastasios G. & Serletis, Apostolos, 2009. "Energy sector pricing: On the role of neglected nonlinearity," Energy Economics, Elsevier, vol. 31(3), pages 492-502, May.
    16. Ramsey James B., 2002. "Wavelets in Economics and Finance: Past and Future," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 6(3), pages 1-29, November.
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    Cited by:

    1. Afees Adebare Salisu & Idris A. Adediran, 2018. "The U.S. Shale Oil Revolution and the Behavior of Commodity Prices," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 3(1), pages 27-53, September.
    2. Alkathery, Mohammed A. & Chaudhuri, Kausik & Nasir, Muhammad Ali, 2023. "Dependence between the GCC energy equities, global clean energy and emission markets: Evidence from wavelet analysis," Energy Economics, Elsevier, vol. 121(C).
    3. Monge, Manuel & Cristóbal, Enrique, 2021. "Terrorism and the behavior of oil production and prices in OPEC," Resources Policy, Elsevier, vol. 74(C).
    4. Monge, Manuel & Gil-Alana, Luis A., 2021. "Lithium industry and the U.S. crude oil prices. A fractional cointegration VAR and a Continuous Wavelet Transform analysis," Resources Policy, Elsevier, vol. 72(C).
    5. Manuel Monge & Luis A. Gil-Alana, 2020. "The Lithium Industry and Analysis of the Beta Term Structure of Oil Companies," Risks, MDPI, vol. 8(4), pages 1-17, December.
    6. Kim, Myung Suk, 2018. "Impacts of supply and demand factors on declining oil prices," Energy, Elsevier, vol. 155(C), pages 1059-1065.
    7. Tiwari, Aviral Kumar & Mukherjee, Zinnia & Gupta, Rangan & Balcilar, Mehmet, 2019. "A wavelet analysis of the relationship between oil and natural gas prices," Resources Policy, Elsevier, vol. 60(C), pages 118-124.
    8. Gil-Alana, Luis A. & Dadgar, Yadollah & Nazari, Rouhollah, 2020. "An analysis of the OPEC and non-OPEC position in the World Oil Market: A fractionally integrated approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    9. Monge, Manuel & Gil-Alana, Luis Alberiko, 2021. "Spatial crude oil production divergence and crude oil price behaviour in the United States," Energy, Elsevier, vol. 232(C).
    10. Edgardo Cayon & Natalia Andrea Garzon & Juan Sebastian Perez, 2019. "The Effects of Global, Regional, and Local Macroeconomic Events on the Price of the Colombian Castilla Blend," International Journal of Energy Economics and Policy, Econjournals, vol. 9(6), pages 118-123.
    11. Zhao, Geya & Xue, Minggao & Cheng, Li, 2023. "A new hybrid model for multi-step WTI futures price forecasting based on self-attention mechanism and spatial–temporal graph neural network," Resources Policy, Elsevier, vol. 85(PB).
    12. Solarin, Sakiru Adebola & Gil-Alana, Luis A. & Lafuente, Carmen, 2020. "An investigation of long range reliance on shale oil and shale gas production in the U.S. market," Energy, Elsevier, vol. 195(C).
    13. Afees A. Salisu & Idris Adediran, 2018. "US shale oil and the behaviour of commodity prices," Working Papers 047, Centre for Econometric and Allied Research, University of Ibadan.
    14. Douglas B. Reynolds, 2024. "U.S. shale oil production and trend estimation: Forecasting a Hubbert model," Economic Inquiry, Western Economic Association International, vol. 62(1), pages 468-487, January.
    15. Jiang, Yong & Zhou, Zhongbao & Liu, Qing & Lin, Ling & Xiao, Helu, 2020. "How do oil price shocks affect the output volatility of the U.S. energy mining industry? The roles of structural oil price shocks," Energy Economics, Elsevier, vol. 87(C).
    16. Zhenhua Liu & Zhihua Ding & Tao Lv & Jy S. Wu & Wei Qiang, 2019. "Financial factors affecting oil price change and oil-stock interactions: a review and future perspectives," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 95(1), pages 207-225, January.
    17. Gkillas, Konstantinos & Manickavasagam, Jeevananthan & Visalakshmi, S., 2022. "Effects of fundamentals, geopolitical risk and expectations factors on crude oil prices," Resources Policy, Elsevier, vol. 78(C).
    18. Ahmad, Shakil, 2021. "Does COVID-19 effects the United States crude oil imports price?," Economic Consultant, Roman I. Ostapenko, vol. 33(1), pages 57-67.
    19. Li, Jinbu & Wang, Min & Jiang, Chunqing & Lu, Shuangfang & Li, Zheng, 2022. "Sorption model of lacustrine shale oil: Insights from the contribution of organic matter and clay minerals," Energy, Elsevier, vol. 260(C).
    20. Bravo Caro, José Manuel & Golpe, Antonio A. & Iglesias, Jesús & Vides, José Carlos, 2020. "A new way of measuring the WTI – Brent spread. Globalization, shock persistence and common trends," Energy Economics, Elsevier, vol. 85(C).
    21. Monge, Manuel & Romero Rojo, María Fátima & Gil-Alana, Luis Alberiko, 2023. "The impact of geopolitical risk on the behavior of oil prices and freight rates," Energy, Elsevier, vol. 269(C).

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    More about this item

    Keywords

    Oil prices; Oil production; Wavelets; Fractional integration;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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