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

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  • Christiane Baumeister
  • Lutz Kilian
  • Xiaoqing Zhou

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 mean-squared prediction error reductions as high as 20 per cent and directional accuracy as high as 63 per cent 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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Bibliographic Info

Paper provided by Bank of Canada in its series Working Papers with number 13-25.

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Length: 42 pages
Date of creation: 2013
Date of revision:
Handle: RePEc:bca:bocawp:13-25

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Keywords: Econometric and statistical methods; International topics;

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References

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  1. Christiane Baumeister & Lutz Kilian, 2012. "Real-Time Analysis of Oil Price Risks Using Forecast Scenarios," Working Papers 12-1, Bank of Canada.
  2. Murat, Atilim & Tokat, Ekin, 2009. "Forecasting oil price movements with crack spread futures," Energy Economics, Elsevier, vol. 31(1), pages 85-90, January.
  3. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  4. Kenneth D. West & Todd Clark, 2006. "Approximately Normal Tests for Equal Predictive Accuracy in Nested Models," NBER Technical Working Papers 0326, National Bureau of Economic Research, Inc.
  5. Baumeister, Christiane & Kilian, Lutz, 2013. "Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach," CEPR Discussion Papers 9569, C.E.P.R. Discussion Papers.
  6. M. Hashem Pesaran & Allan Timmermann, 2006. "Testing Dependence among Serially Correlated Multi-category Variables," CESifo Working Paper Series 1770, CESifo Group Munich.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. Menzie D. Chinn & Olivier Coibion, 2010. "The Predictive Content of Commodity Futures," NBER Working Papers 15830, National Bureau of Economic Research, Inc.
  14. 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.
  15. 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.
  16. Christiane Baumeister & Lutz Kilian, 2013. "What Central Bankers Need to Know about Forecasting Oil Prices," Working Papers 13-15, Bank of Canada.
  17. 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.
  18. 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.
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Cited by:
  1. Baumeister, Christiane & Kilian, Lutz, 2013. "Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach," CEPR Discussion Papers 9569, C.E.P.R. Discussion Papers.

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