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Are Product Spreads Useful for Forecasting? An Empirical Evaluation of the Verleger Hypothesis

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  • Baumeister, Christiane
  • Kilian, Lutz
  • Zhou, Xiaoqing

Abstract

Notwithstanding a resurgence in research on out-of-sample forecasts of the price of oil in recent years, there is one important approach to forecasting the real price of oil which has not been studied systematically to date. This approach is based on the premise that demand for crude oil derives from the demand for refined products such as gasoline or heating oil. Oil industry analysts such as Philip Verleger and financial analysts widely believe that there is predictive power in the product spread, defined as the difference between suitably weighted refined product market prices and the price of crude oil. Our objective is to evaluate this proposition. We derive from first principles a number of alternative forecasting model specifications involving product spreads and compare these models to the no-change forecast of the real price of oil. We show that not all product spread models are useful for out-of-sample forecasting, but some models are, even at horizons between one and two years. The most accurate model is a time-varying parameter model of gasoline and heating oil spot spreads that allows the marginal product market to change over time. We document MSPE reductions as high as 20% and directional accuracy as high as 63% at the two-year horizon, making product spread models a good complement to forecasting models based on economic fundamentals, which work best at short horizons.

Suggested Citation

  • Baumeister, Christiane & Kilian, Lutz & Zhou, Xiaoqing, 2013. "Are Product Spreads Useful for Forecasting? An Empirical Evaluation of the Verleger Hypothesis," CEPR Discussion Papers 9572, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:9572
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    References listed on IDEAS

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    1. Trevor A. Reeve & Robert J. Vigfusson, 2011. "Evaluating the forecasting performance of commodity futures prices," International Finance Discussion Papers 1025, Board of Governors of the Federal Reserve System (U.S.).
    2. Kilian, Lutz, 1999. "Exchange Rates and Monetary Fundamentals: What Do We Learn from Long-Horizon Regressions?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 491-510, Sept.-Oct.
    3. Ron Alquist & Lutz Kilian, 2010. "What do we learn from the price of crude oil futures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 539-573.
    4. Christiane Baumeister & Lutz Kilian, 2014. "Real-Time Analysis of Oil Price Risks Using Forecast Scenarios," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 62(1), pages 119-145, April.
    5. Christiane Baumeister & Lutz Kilian, 2015. "Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 338-351, July.
    6. Christiane Baumeister & Lutz Kilian, 2014. "What Central Bankers Need To Know About Forecasting Oil Prices," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55, pages 869-889, August.
    7. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    8. Murat, Atilim & Tokat, Ekin, 2009. "Forecasting oil price movements with crack spread futures," Energy Economics, Elsevier, vol. 31(1), pages 85-90, January.
    9. Thomas A. Knetsch, 2007. "Forecasting the price of crude oil via convenience yield predictions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(7), pages 527-549.
    10. Clark, Todd E. & McCracken, Michael W., 2009. "Tests of Equal Predictive Ability With Real-Time Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 441-454.
    11. Christiane Baumeister & Lutz Kilian, 2011. "Real-Time Forecasts of the Real Price of Oil," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 326-336, September.
    12. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    13. 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.
    14. Pesaran, M. Hashem & Timmermann, Allan, 2009. "Testing Dependence Among Serially Correlated Multicategory Variables," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 325-337.
    15. Michael S. Haigh & Matthew T. Holt, 2002. "Crack spread hedging: accounting for time-varying volatility spillovers in the energy futures markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(3), pages 269-289.
    16. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    17. Lowinger, Thomas C & Ram, Rati, 1984. "Product Value as a Determinant of OPEC's Official Crude Oil Prices: Additional Evidence [The Determinants of Official OPEC Crude Prices]," The Review of Economics and Statistics, MIT Press, vol. 66(4), pages 691-695, November.
    18. Lanza, Alessandro & Manera, Matteo & Giovannini, Massimo, 2005. "Modeling and forecasting cointegrated relationships among heavy oil and product prices," Energy Economics, Elsevier, vol. 27(6), pages 831-848, November.
    19. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
    20. Mark, Nelson C, 1995. "Exchange Rates and Fundamentals: Evidence on Long-Horizon Predictability," American Economic Review, American Economic Association, vol. 85(1), pages 201-218, March.
    21. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388.
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    Citations

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    Cited by:

    1. Van Robays, Ine & Belu Mănescu, Cristiana, 2014. "Forecasting the Brent oil price: addressing time-variation in forecast performance," Working Paper Series 1735, European Central Bank.
    2. Baumeister, Christiane & Kilian, Lutz & Lee, Thomas K., 2014. "Are there gains from pooling real-time oil price forecasts?," Energy Economics, Elsevier, vol. 46(S1), pages 33-43.
    3. Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2015. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," International Journal of Forecasting, Elsevier, vol. 31(2), pages 238-252.
    4. Christiane Baumeister & Lutz Kilian, 2015. "Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 338-351, July.
    5. Christiane Baumeister & Lutz Kilian, 2014. "Do oil price increases cause higher food prices?," Economic Policy, CEPR;CES;MSH, vol. 29(80), pages 691-747, October.
    6. Christiane Baumeister & Lutz Kilian & Thomas K. Lee, 2017. "Inside the Crystal Ball: New Approaches to Predicting the Gasoline Price at the Pump," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 275-295, March.
    7. Zhang, Hui Jun & Dufour, Jean-Marie & Galbraith, John W., 2016. "Exchange rates and commodity prices: Measuring causality at multiple horizons," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 100-120.
    8. Bastianin, Andrea & Galeotti, Marzio & Manera, Matteo, 2014. "Forecasting the oil–gasoline price relationship: Do asymmetries help?," Energy Economics, Elsevier, vol. 46(S1), pages 44-56.
    9. Jean-Thomas Bernard & Lynda Khalaf & Maral Kichian & Clement Yelou, 2015. "Oil Price Forecasts for the Long-Term: Expert Outlooks, Models, or Both?," Working Papers 1510E, University of Ottawa, Department of Economics.
    10. repec:gam:jsusta:v:10:y:2018:i:2:p:454-:d:131091 is not listed on IDEAS
    11. Arunanondchai, Panit & Senia, Mark C. & Capps, Oral Jr, 2017. "Can U.S. EIA Retail Gasoline Price Forecasts Be Improved Upon?," 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama 252717, Southern Agricultural Economics Association.
    12. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil prices," MPRA Paper 77531, University Library of Munich, Germany.
    13. Elder, John & Miao, Hong & Ramchander, Sanjay, 2014. "Price discovery in crude oil futures," Energy Economics, Elsevier, vol. 46(S1), pages 18-27.

    More about this item

    Keywords

    acquisition cost; crack spread; forecast accuracy; futures; oil price; real-time data; refined products; WTI;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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