IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i10p848-d81045.html
   My bibliography  Save this article

Revenue Risk of U.S. Tight-Oil Firms

Author

Listed:
  • Luis Mª Abadie

    (Basque Centre for Climate Change, Sede Building 1, 1st floor, Scientific Campus of the University of the Basque Country, 48940 Leioa, Spain)

  • José M. Chamorro

    (Department of Financial Economics II, University of the Basque Country, Av. Lehendakari Aguirre 83, 48015 Bilbao, Spain)

Abstract

American U.S. crude oil prices have dropped significantly of late down to a low of less than $30 a barrel in early 2016. At the same time price volatility has increased and crude in storage has reached record amounts in the U.S. America. Low oil prices in particular pose quite a challenge for the survival of U.S. America’s tight-oil industry. In this paper we assess the current profitability and future prospects of this industry. The question could be broadly stated as: should producers stop operation immediately or continue in the hope that prices will rise in the medium term? Our assessment is based on a stochastic volatility model with three risk factors, namely the oil spot price, the long-term oil price, and the spot price volatility; we allow for these sources of risk to be correlated and display mean reversion. We then use information from spot and futures West Texas Intermediate (WTI) oil prices to estimate this model. Our aim is to show how the development of the oil price in the future may affect the prospective revenues of firms and hence their operation decisions at present. With the numerical estimates of the model’s parameters we can compute the value of an operating tight-oil field over a certain time horizon. Thus, the present value (PV) of the prospective revenues up to ten years from now is $37.07/bbl in the base case. Consequently, provided that the cost of producing a barrel of oil is less than $37.07 production from an operating field would make economic sense. Obviously this is just a point estimate. We further perform a Monte Carlo (MC) simulation to derive the risk profile of this activity and calculate two standard measures of risk, namely the value at risk (VaR) and the expected shortfall (ES) (for a given confidence level). In this sense, the PV of the prospective revenues will fall below $22.22/bbl in the worst 5% of the cases; and the average value across these worst scenarios is $19.77/bbl. Last we undertake two sensitivity analyses with respect to the spot price and the long-term price. The former is shown to have a stronger impact on the field’s value than the latter. This bodes well with the usual time profile of tight oil production: intense depletion initially, followed by steep decline thereafter.

Suggested Citation

  • Luis Mª Abadie & José M. Chamorro, 2016. "Revenue Risk of U.S. Tight-Oil Firms," Energies, MDPI, vol. 9(10), pages 1-18, October.
  • Handle: RePEc:gam:jeners:v:9:y:2016:i:10:p:848-:d:81045
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/10/848/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/9/10/848/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christiane Baumeister & Lutz Kilian, 2016. "Forty Years of Oil Price Fluctuations: Why the Price of Oil May Still Surprise Us," Journal of Economic Perspectives, American Economic Association, vol. 30(1), pages 139-160, Winter.
    2. Congressional Budget Office, 2014. "The Economic and Budgetary Effects of Producing Oil and Natural Gas From Shale," Reports 49815, Congressional Budget Office.
    3. Congressional Budget Office, 2014. "The Economic and Budgetary Effects of Producing Oil and Natural Gas From Shale," Reports 49815, Congressional Budget Office.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Luis Mª Abadie & José M. Chamorro, 2017. "Valuation of Real Options in Crude Oil Production," Energies, MDPI, vol. 10(8), pages 1-21, August.
    2. Josué M. Polanco-Martínez & Luis M. Abadie, 2016. "Analyzing Crude Oil Spot Price Dynamics versus Long Term Future Prices: A Wavelet Analysis Approach," Energies, MDPI, vol. 9(12), pages 1-19, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Smith, James L. & Lee, Thomas K., 2017. "The price elasticity of U.S. shale oil reserves," Energy Economics, Elsevier, vol. 67(C), pages 121-135.
    2. Catherine Hausman & Ryan Kellogg, 2015. "Welfare and Distributional Implications of Shale Gas," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 46(1 (Spring), pages 71-139.
    3. Arora, Vipin, 2017. "Shale and the US Economy: Three Counterfactuals," MPRA Paper 79672, University Library of Munich, Germany.
    4. Mr. Benjamin L Hunt & Mr. Dirk V Muir & Mr. Martin Sommer, 2015. "The Potential Macroeconomic Impact of the Unconventional Oil and Gas Boom in the United States," IMF Working Papers 2015/092, International Monetary Fund.
    5. Joao Ayres & Constantino Hevia & Juan Pablo Nicolini, 2021. "Real Exchange Rates and Primary Commodity Prices: Mussa Meets Backus-Smith," Working Papers 89, Red Nacional de Investigadores en Economía (RedNIE).
    6. Jen-Yu Lee & Tien-Thinh Nguyen & Hong-Giang Nguyen & Jen-Yao Lee, 2022. "Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe," Energies, MDPI, vol. 15(11), pages 1-15, May.
    7. Pham T. T. Trinh & Bui T. T. My, 2023. "The impact of world oil price shocks on macroeconomic variables in Vietnam: the transmission through domestic oil price," Asian-Pacific Economic Literature, The Crawford School, The Australian National University, vol. 37(1), pages 67-87, May.
    8. Beckmann, Joscha & Czudaj, Robert L. & Arora, Vipin, 2020. "The relationship between oil prices and exchange rates: Revisiting theory and evidence," Energy Economics, Elsevier, vol. 88(C).
    9. An, Zidong & Binder, Carola & Sheng, Xuguang Simon, 2023. "Gas price expectations of Chinese households," Energy Economics, Elsevier, vol. 120(C).
    10. Gupta, Rangan & Wohar, Mark, 2017. "Forecasting oil and stock returns with a Qual VAR using over 150years off data," Energy Economics, Elsevier, vol. 62(C), pages 181-186.
    11. Emmanuel Apergis & Nicholas Apergis, 2021. "The impact of COVID-19 on economic growth: evidence from a Bayesian Panel Vector Autoregressive (BPVAR) model," Applied Economics, Taylor & Francis Journals, vol. 53(58), pages 6739-6751, December.
    12. Hicham Ayad & Ousama Ben-Salha & Miloud Ouafi, 2023. "Do oil prices predict the exchange rate in Algeria? Time, frequency, and time‐varying Granger causality analysis," Economic Change and Restructuring, Springer, vol. 56(5), pages 3545-3566, October.
    13. Benk, Szilard & Gillman, Max, 2020. "Granger predictability of oil prices after the Great Recession," Journal of International Money and Finance, Elsevier, vol. 101(C).
    14. Dalheimer, Bernhard & Herwartz, Helmut & Lange, Alexander, 2021. "The threat of oil market turmoils to food price stability in Sub-Saharan Africa," Energy Economics, Elsevier, vol. 93(C).
    15. Ahmed, Abdullahi D. & Huo, Rui, 2021. "Volatility transmissions across international oil market, commodity futures and stock markets: Empirical evidence from China," Energy Economics, Elsevier, vol. 93(C).
    16. Mahadeo, Scott M.R. & Heinlein, Reinhold & Legrenzi, Gabriella D., 2019. "Energy contagion analysis: A new perspective with application to a small petroleum economy," Energy Economics, Elsevier, vol. 80(C), pages 890-903.
    17. Liu, Yang & Han, Liyan & Xu, Yang, 2021. "The impact of geopolitical uncertainty on energy volatility," International Review of Financial Analysis, Elsevier, vol. 75(C).
    18. Valenti, Daniele & Bastianin, Andrea & Manera, Matteo, 2023. "A weekly structural VAR model of the US crude oil market," Energy Economics, Elsevier, vol. 121(C).
    19. David Martimort & Jerome Pouyet & Francesco Ricci, 2018. "Contracts for the Management of a Non-Renewable Resource under Asymmetric Information and Structural Price Breaks," Annals of Economics and Statistics, GENES, issue 132, pages 81-103.
    20. Qi Zhang & Yi Hu & Jianbin Jiao & Shouyang Wang, 2022. "Exploring the Trend of Commodity Prices: A Review and Bibliometric Analysis," Sustainability, MDPI, vol. 14(15), pages 1-22, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:9:y:2016:i:10:p:848-:d:81045. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.