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Are product spreads useful for forecasting? An empirical evaluation of the Verleger hypothesis

  • Baumeister, Christiane
  • Kilian, Lutz

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.

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Paper provided by Center for Financial Studies (CFS) in its series CFS Working Paper Series with number 2013/09.

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Date of creation: 2013
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Handle: RePEc:zbw:cfswop:201309
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  1. Christiane Baumeister & Lutz Kilian, 2011. "Real-Time Forecasts of the Real Price of Oil," Working Papers 11-16, Bank of Canada.
  2. 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, 08.
  3. Baumeister, Christiane & Kilian, Lutz, 2011. "Real-Time Analysis of Oil Price Risks Using Forecast Scenarios," CEPR Discussion Papers 8698, C.E.P.R. Discussion Papers.
  4. Todd E. Clark & Kenneth D. West, 2005. "Approximately normal tests for equal predictive accuracy in nested models," Research Working Paper RWP 05-05, Federal Reserve Bank of Kansas City.
  5. Murat, Atilim & Tokat, Ekin, 2009. "Forecasting oil price movements with crack spread futures," Energy Economics, Elsevier, vol. 31(1), pages 85-90, January.
  6. Mark, Nelson C, 1995. "Exchange Rates and Fundamentals: Evidence on Long-Horizon Predictability," American Economic Review, American Economic Association, vol. 85(1), pages 201-18, March.
  7. Christiane Baumeister & Lutz Kilian, 2013. "Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach," Working Papers 13-28, Bank of Canada.
  8. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  9. Knetsch, Thomas A., 2006. "Forecasting the price of crude oil via convenience yield predictions," Discussion Paper Series 1: Economic Studies 2006,12, Deutsche Bundesbank, Research Centre.
  10. Menzie D. Chinn & Olivier Coibion, 2010. "The Predictive Content of Commodity Futures," NBER Working Papers 15830, National Bureau of Economic Research, Inc.
  11. Pesaran, M. Hashem & Timmermann, Allan, 2006. "Testing Dependence among Serially Correlated Multi-Category Variables," IZA Discussion Papers 2196, Institute for the Study of Labor (IZA).
  12. 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 691-95, November.
  13. 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.
  14. Todd E. Clark & Michael W. McCracken, 2007. "Tests of equal predictive ability with real-time data," Research Working Paper RWP 07-06, Federal Reserve Bank of Kansas City.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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, June.
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