IDEAS home Printed from https://ideas.repec.org/p/fem/femwpa/2007.4.html
   My bibliography  Save this paper

Evaluating the Empirical Performance of Alternative Econometric Models for Oil Price Forecasting

Author

Listed:
  • Matteo Manera

    (University of Milan Bicocca)

  • Chiara Longo

    (Fondazione Eni Enrico Mattei)

  • Anil Markandya

    (University of Bath and Fondazione Eni Enrico Mattei)

  • Elisa Scarpa

    (Risk Management Department, Intesa-San Paolo)

Abstract

The relevance of oil in the world economy explains why considerable effort has been devoted to the development of different types of econometric models for oil price forecasting. Several specifications have been proposed in the economic literature. Some are based on financial theory and concentrate on the relationship between spot and futures prices (“financial” models). Others assign a key role to variables explaining the characteristics of the physical oil market (“structural” models). The empirical literature is very far from any consensus about the appropriate model for oil price forecasting that should be implemented. Relative to the previous literature, this paper is novel in several respects. First of all, we test and systematically evaluate the ability of several alternative econometric specifications proposed in the literature to capture the dynamics of oil prices. Second, we analyse the effects of different data frequencies on the coefficient estimates and forecasts obtained using each selected econometric specification. Third, we compare different models at different data frequencies on a common sample and common data. Fourth, we evaluate the forecasting performance of each selected model using static and dynamic forecasts, as well as different measures of forecast errors. Finally, we propose a new class of models which combine the relevant aspects of the financial and structural specifications proposed in the literature (“mixed” models). Our empirical findings can be summarized as follows. Financial models in levels do not produce satisfactory forecasts for the WTI spot price. The financial error correction model yields accurate in-sample forecasts. Real and strategic variables alone are insufficient to capture the oil spot price dynamics in the forecasting sample. Our proposed mixed models are statistically adequate and exhibit accurate forecasts. Different data frequencies seem to affect the forecasting ability of the models under analysis.

Suggested Citation

  • Matteo Manera & Chiara Longo & Anil Markandya & Elisa Scarpa, 2007. "Evaluating the Empirical Performance of Alternative Econometric Models for Oil Price Forecasting," Working Papers 2007.4, Fondazione Eni Enrico Mattei.
  • Handle: RePEc:fem:femwpa:2007.4
    as

    Download full text from publisher

    File URL: https://feem-media.s3.eu-central-1.amazonaws.com/wp-content/uploads/NDL2007-004.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Gulen, S. Gurcan, 1998. "Efficiency in the crude oil futures market," Journal of Energy Finance & Development, Elsevier, vol. 3(1), pages 13-21.
    2. Dees, Stephane & Karadeloglou, Pavlos & Kaufmann, Robert K. & Sanchez, Marcelo, 2007. "Modelling the world oil market: Assessment of a quarterly econometric model," Energy Policy, Elsevier, vol. 35(1), pages 178-191, January.
    3. Menzie D. Chinn & Michael LeBlanc & Olivier Coibion, 2005. "The Predictive Content of Energy Futures: An Update on Petroleum, Natural Gas, Heating Oil and Gasoline," NBER Working Papers 11033, National Bureau of Economic Research, Inc.
    4. Apostolos Serletis, 2007. "Rational Expectations, Risk, and Efficiency in Energy Futures Markets," World Scientific Book Chapters, in: Quantitative And Empirical Analysis Of Energy Markets, chapter 2, pages 15-22, World Scientific Publishing Co. Pte. Ltd..
    5. Kaufmann, Robert K., 1995. "A model of the world oil market for project LINK Integrating economics, geology and politics," Economic Modelling, Elsevier, vol. 12(2), pages 165-178, April.
    6. Stanislav Radchenko, 2005. "The Long-Run Forecasting of Energy Prices Using the Model of Shifting Trend," Econometrics 0502002, University Library of Munich, Germany.
    7. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    8. John R. Moroney & M. Douglas Berg, 1999. "An Integrated Model of Oil Production," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 105-124.
    9. Zeng Tian & Swanson Norman R., 1998. "Predictive Evaluation of Econometric Forecasting Models in Commodity Futures Markets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 2(4), pages 1-21, January.
    10. Saeed Moshiri & Faezeh Foroutan, 2006. "Forecasting Nonlinear Crude Oil Futures Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 81-96.
    11. Bopp, Anthony E. & Lady, George M., 1991. "A comparison of petroleum futures versus spot prices as predictors of prices in the future," Energy Economics, Elsevier, vol. 13(4), pages 274-282, October.
    12. MacKinnon, James G, 1996. "Numerical Distribution Functions for Unit Root and Cointegration Tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 601-618, Nov.-Dec..
    13. James G. MacKinnon, 1990. "Critical Values for Cointegration Tests," Working Paper 1227, Economics Department, Queen's University.
    14. Moosa, Imad A. & Al-Loughani, Nabeel E., 1994. "Unbiasedness and time varying risk premia in the crude oil futures market," Energy Economics, Elsevier, vol. 16(2), pages 99-105, April.
    15. Green, Steven L & Mork, Knut Anton, 1991. "Toward Efficiency in the Crude-Oil Market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(1), pages 45-66, Jan.-Marc.
    16. Robert K. Kaufmann, Stephane Dees, Pavlos Karadeloglou and Marcelo Sanchez, 2004. "Does OPEC Matter? An Econometric Analysis of Oil Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 67-90.
    17. Michael Ye & John Zyren & Joanne Shore, 2002. "Forecasting crude oil spot price using OECD petroleum inventory levels," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 8(4), pages 324-333, November.
    18. Ye, Michael & Zyren, John & Shore, Joanne, 2005. "A monthly crude oil spot price forecasting model using relative inventories," International Journal of Forecasting, Elsevier, vol. 21(3), pages 491-501.
    19. Antonio Merino & Álvaro Ortiz, 2005. "Explaining the so‐called “price premium” in oil markets," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 29(2), pages 133-152, June.
    20. Robert S. Pindyck, 1999. "The Long-Run Evolutions of Energy Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 1-27.
    21. Salah Abosedra, 2005. "Futures versus univariate forecast of crude oil prices," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 29(4), pages 231-241, December.
    22. Morana, Claudio, 2001. "A semiparametric approach to short-term oil price forecasting," Energy Economics, Elsevier, vol. 23(3), pages 325-338, May.
    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. Zhang, Yue-Jun & Yao, Ting & He, Ling-Yun & Ripple, Ronald, 2019. "Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models?," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 302-317.
    2. Jammazi, Rania, 2012. "Oil shock transmission to stock market returns: Wavelet-multivariate Markov switching GARCH approach," Energy, Elsevier, vol. 37(1), pages 430-454.
    3. Slabá, Monika & Gapko, Petr & Klimešová, Andrea, 2013. "Main drivers of natural gas prices in the Czech Republic after the market liberalisation," Energy Policy, Elsevier, vol. 52(C), pages 199-212.
    4. Clostermann, Jörg & Keis, Nikolaus & Seitz, Franz, 2010. "Short-term oil models before and during the financial market crisis," Arbeitsberichte – Working Papers 18, Technische Hochschule Ingolstadt (THI).
    5. Mamatzakis, E. & Koutsomanoli-Filippaki, A., 2014. "Testing the rationality of DOE's energy price forecasts under asymmetric loss preferences," Energy Policy, Elsevier, vol. 68(C), pages 567-575.
    6. Ekins, Paul & Pollitt, Hector & Barton, Jennifer & Blobel, Daniel, 2011. "The implications for households of environmental tax reform (ETR) in Europe," Ecological Economics, Elsevier, vol. 70(12), pages 2472-2485.
    7. He, Yanan & Wang, Shouyang & Lai, Kin Keung, 2010. "Global economic activity and crude oil prices: A cointegration analysis," Energy Economics, Elsevier, vol. 32(4), pages 868-876, July.
    8. Yue-Jun Zhang & Ting Yao & Ling-Yun He, 2015. "Forecasting crude oil market volatility: can the Regime Switching GARCH model beat the single-regime GARCH models?," Papers 1512.01676, arXiv.org.
    9. Dale Roberts & Laura Ryan, 2015. "Evidence of speculation in world oil prices," Australian Journal of Management, Australian School of Business, vol. 40(4), pages 630-651, November.
    10. Claudio Dicembrino & Pasquale Lucio Scandizzo, 2012. "The Fundamental and Speculative Components of the Oil Spot Price: A Real Option Value Approach," CEIS Research Paper 229, Tor Vergata University, CEIS, revised 18 Apr 2012.
    11. Saporta, Victoria & Trott, Matt & Tudela, Merxe, 2009. "What can be said about the rise and fall in oil prices?," Bank of England Quarterly Bulletin, Bank of England, vol. 49(3), pages 215-225.
    12. Zhang, Xun & Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2009. "Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method," Energy Economics, Elsevier, vol. 31(5), pages 768-778, September.
    13. Sanders, Dwight R. & Manfredo, Mark R. & Boris, Keith, 2009. "Evaluating information in multiple horizon forecasts: The DOE's energy price forecasts," Energy Economics, Elsevier, vol. 31(2), pages 189-196.
    14. Marcos Álvarez-Díaz, 2020. "Is it possible to accurately forecast the evolution of Brent crude oil prices? An answer based on parametric and nonparametric forecasting methods," Empirical Economics, Springer, vol. 59(3), pages 1285-1305, September.
    15. Ai Han & Yanan He & Yongmiao Hong & Shouyang Wang, 2013. "Forecasting Interval-valued Crude Oil Prices via Autoregressive Conditional Interval Models," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.

    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. Giliola Frey & Matteo Manera & Anil Markandya & Elisa Scarpa, 2009. "Econometric Models for Oil Price Forecasting: A Critical Survey," CESifo Forum, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 10(1), pages 29-44, April.
    2. Bastianin, Andrea & Manera, Matteo & Markandya, Anil & Scarpa, Elisa, 2011. "Oil Price Forecast Evaluation with Flexible Loss Functions," Energy: Resources and Markets 120042, Fondazione Eni Enrico Mattei (FEEM).
    3. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    4. Claudio Dicembrino & Pasquale Lucio Scandizzo, 2012. "The Fundamental and Speculative Components of the Oil Spot Price: A Real Option Value Approach," CEIS Research Paper 229, Tor Vergata University, CEIS, revised 18 Apr 2012.
    5. Chen, Shyh-Wei & Lin, Shih-Mo, 2014. "Non-linear dynamics in international resource markets: Evidence from regime switching approach," Research in International Business and Finance, Elsevier, vol. 30(C), pages 233-247.
    6. Giliola Frey & Matteo Manera & Anil Markandya & Elisa Scarpa, 2009. "Econometric Models for Oil Price Forecasting: A Critical Survey," CESifo Forum, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 10(01), pages 29-44, April.
    7. Naser, Hanan, 2016. "Estimating and forecasting the real prices of crude oil: A data rich model using a dynamic model averaging (DMA) approach," Energy Economics, Elsevier, vol. 56(C), pages 75-87.
    8. Ai Han & Yanan He & Yongmiao Hong & Shouyang Wang, 2013. "Forecasting Interval-valued Crude Oil Prices via Autoregressive Conditional Interval Models," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    9. Jeffrey A Frankel & Andrew K Rose, 2010. "Determinants of Agricultural and Mineral Commodity Prices," RBA Annual Conference Volume (Discontinued), in: Renée Fry & Callum Jones & Christopher Kent (ed.),Inflation in an Era of Relative Price Shocks, Reserve Bank of Australia.
    10. Frankel, Jeffrey A., 2014. "Effects of speculation and interest rates in a “carry trade” model of commodity prices," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 88-112.
    11. Chevillon, Guillaume & Rifflart, Christine, 2009. "Physical market determinants of the price of crude oil and the market premium," Energy Economics, Elsevier, vol. 31(4), pages 537-549, July.
    12. Manuel Frondel and Marco Horvath, 2019. "The U.S. Fracking Boom: Impact on Oil Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    13. Clostermann, Jörg & Keis, Nikolaus & Seitz, Franz, 2010. "Short-term oil models before and during the financial market crisis," Arbeitsberichte – Working Papers 18, Technische Hochschule Ingolstadt (THI).
    14. Salah Abosedra, 2005. "Futures versus univariate forecast of crude oil prices," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 29(4), pages 231-241, December.
    15. Theodore Syriopoulos & Michael Tsatsaronis & Ioannis Karamanos, 2021. "Support Vector Machine Algorithms: An Application to Ship Price Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 55-87, January.
    16. Charfeddine, Lanouar & Khediri, Karim Ben & Mrabet, Zouhair, 2019. "The forward premium anomaly in the energy futures markets: A time-varying approach," Research in International Business and Finance, Elsevier, vol. 47(C), pages 600-615.
    17. Menzie D. Chinn & Olivier Coibion, 2014. "The Predictive Content of Commodity Futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 34(7), pages 607-636, July.
    18. He, Yanan & Wang, Shouyang & Lai, Kin Keung, 2010. "Global economic activity and crude oil prices: A cointegration analysis," Energy Economics, Elsevier, vol. 32(4), pages 868-876, July.
    19. Fan, Ying & Liang, Qiang & Wei, Yi-Ming, 2008. "A generalized pattern matching approach for multi-step prediction of crude oil price," Energy Economics, Elsevier, vol. 30(3), pages 889-904, May.
    20. Latife Ghalayini, 2017. "Modeling and forecasting spot oil price," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 7(3), pages 355-373, December.

    More about this item

    Keywords

    Oil Price; WTI Spot And Futures Prices; Forecasting; Econometric Models;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q32 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Exhaustible Resources and Economic Development
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:fem:femwpa:2007.4. 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: Alberto Prina Cerai (email available below). General contact details of provider: https://edirc.repec.org/data/feemmit.html .

    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.