Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-22T19:24:45.183Z Has data issue: false hasContentIssue false
  • English
  • Français

ARE PRODUCT SPREADS USEFUL FOR FORECASTING OIL PRICES? AN EMPIRICAL EVALUATION OF THE VERLEGER HYPOTHESIS

Published online by Cambridge University Press:  10 August 2017

Christiane Baumeister ,
Lutz Kilian  and
Xiaoqing Zhou
Christiane Baumeister
Affiliation:
University of Notre Dame
Lutz Kilian*
Affiliation:
University of Michigan
Xiaoqing Zhou
Affiliation:
University of Michigan
*
Address correspondence to: Lutz Kilian, Department of Economics, University of Michigan, 309 Lorch Hall, Ann Arbor, MI 48109-1220, USA; e-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Many oil industry analysts 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. We derive a number of alternative forecasting model specifications based on product spreads and compare the implied forecasts 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 price spreads that allows for structural change in product markets. We document mean-squared prediction error 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.

Keywords

Oil PriceRefined ProductsCrack SpreadForecast Accuracy
Type
Articles
Information
Macroeconomic Dynamics , Volume 22 , Special Issue 3: Dynamics of Oil and Commodities Prices , April 2018 , pp. 562 - 580
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

We thank Ron Alquist, Bahattin Büyüksahin, Barbara Rossi, Philip K. Verleger, and Jonathan Wright for helpful comments. Jamshid Mavalwalla provided excellent research assistance.

References

REFERENCES

Alquist, Ron and Kilian, Lutz (2010) What do we learn from the price of crude oil futures? Journal of Applied Econometrics 25, 539–573.Google Scholar
Alquist, Ron, Kilian, Lutz, and Vigfusson, Robert J. (2013) Forecasting the price of oil. In Elliott, Graham and Timmermann, Allan (eds.), Handbook of Economic Forecasting, vol. 2, pp. 427–507. Amsterdam: North-Holland.Google Scholar
Baumeister, Christiane, Guérin, Pierre, and Kilian, Lutz (2015) Do high-frequency financial data help forecast oil prices? The MIDAS touch at work. International Journal of Forecasting 31, 238–252.Google Scholar
Baumeister, Christiane and Kilian, Lutz (2012) Real-time forecasts of the real price of oil. Journal of Business and Economic Statistics 30, 326–336.CrossRefGoogle Scholar
Baumeister, Christiane and Kilian, Lutz (2014a) Real-time analysis of oil price risks using forecast scenarios. IMF Economic Review 62, 119–145.CrossRefGoogle Scholar
Baumeister, Christiane and Kilian, Lutz (2014b) What central bankers need to know about forecasting oil prices. International Economic Review 55, 869–889.Google Scholar
Baumeister, Christiane and Kilian, Lutz (2015) Forecasting the real price of oil in a changing world: A forecast combination approach. Journal of Business and Economic Statistics 33, 338–351.Google Scholar
Baumeister, Christiane, Kilian, Lutz, and Lee, Thomas K. (2014) Are there gains from pooling real-time oil price forecasts? Energy Economics 46, S33–S43.Google Scholar
Bernard, Jean-Thomas, Khalaf, Lynda, Kichian, Maral, and Yelou, Clement (in press) Oil price forecasts for the long term: Expert outlooks, models, or both? Macroeconomics Dynamics.Google Scholar
Brown, Stephen P. A. and Virmani, Raghav (2007) What's driving gasoline prices? Economic Letter. 2, 1–8.Google Scholar
Chen, Yu-Chin, Rogoff, Kenneth S., and Rossi, Barbara (2010) Can exchange rates forecast commodity prices? Quarterly Journal of Economics 125, 1145–1194.Google Scholar
Chicago Mercantile Exchange (2012) Introduction to crack spreads. In Crack Spread Handbook. Chicago, IL: The CME Group. Available at: cmegroup.com/energy.Google Scholar
Clark, Todd E. and McCracken, Michael W. (2009) Tests of equal predictive ability with real-time data. Journal of Business and Economic Statistics 27, 441–454.Google Scholar
Clark, Todd E. and West, Kenneth D. (2007) Approximately normal tests for equal predictive accuracy in nested models. Journal of Econometrics 138, 291–311.Google Scholar
Diebold, Francis X. and Mariano, Roberto S. (1995) Comparing predictive accuracy. Journal of Business and Economic Statistics 13, 253–263.Google Scholar
Diebold, Francis X. and Pauly, Peter (1987) Structural change and the combination of forecasts. Journal of Forecasting 6, 21–40.Google Scholar
Evans, Beth (2009) Oil Market ‘Teetering on the Edge’, Warns Verleger. Available at: http://blogs.platts.com/2009/09/28/oil_market_teet, last accessed September 28.Google Scholar
Faust, Jon and Wright, Jonathan H. (2013) Forecasting inflation. In Elliott, Graham and Timmermann, Allan (eds.), Handbook of Economic Forecasting, vol. 2, pp. 3–56. Amsterdam: North-Holland.Google Scholar
Haigh, Michael S. and Holt, Matthew (2002) Crack spread hedging: Accounting for time-varying volatility spillovers in the energy futures markets. Journal of Applied Econometrics 17, 269–289.Google Scholar
Inoue, Atsushi and Kilian, Lutz (2004) In-sample or out-of-sample tests of predictability: Which one Should we use? Econometric Reviews 23, 371–402.CrossRefGoogle Scholar
Kilian, Lutz (1999) Exchange rates and monetary fundamentals: What do we learn from long-horizon regressions? Journal of Applied Econometrics 14, 491–510.3.0.CO;2-D>CrossRefGoogle Scholar
Kilian, Lutz (2010) Explaining fluctuations in U.S. gasoline prices: A joint model of the global crude oil market and the U.S. retail gasoline market. Energy Journal 31, 87–104.Google Scholar
Kilian, Lutz (2015) Comment on Francis X. Diebold's ‘Comparing predictive accuracy, twenty years later: A personal perspective on the use and abuse of Diebold-Mariano tests’. Journal of Business and Economic Statistics 33, 13–17.CrossRefGoogle Scholar
Kilian, L. (2016) The impact of the shale oil revolution on U.S. oil and gas prices. Review of Environmental Economics and Policy 10, 185–205.Google Scholar
Kim, Chang-Jin and Nelson, Charles R. (1999) State Space Models with Regime Switching: Classical and Gibbs Sampling Approaches with Applications. Cambridge, MA: MIT Press.Google Scholar
Knetsch, Thomas A. (2007) Forecasting the price of oil via convenience yield predictions. Journal of Forecasting 26, 527–549.Google Scholar
Lanza, Alessandro, Manera, Matteo, and Giovannini, Massimo (2005) Modeling and forecasting cointegrated relationships among heavy oil and product prices. Energy Economics 27, 831–848.Google Scholar
Lowinger, Thomas C. and Ram, Rati (1984) Product value as a determinant of OPEC's official crude oil prices: Additional evidence. Review of Economics and Statistics 66, 691–695.CrossRefGoogle Scholar
Mark, Nelson C. (1995) Exchange rates and fundamentals: Evidence on long-horizon predictability. American Economic Review 85, 201–218.Google Scholar
Moors, Kent (2011) Crack Spreads, Oil Futures and $5 Gasoline. Oil and Energy Investor. Available at: http://oilandenergyinvestor.com/2011/01/crack-spreads-oil-futures-and-5-gasoline/.Google Scholar
Murat, Atilim and Tokat, Ekin (2009) Forecasting oil price moveaments with crack spread futures. Energy Economics 31, 85–90.Google Scholar
Pesaran, M. Hashem and Timmermann, Allan (2009) Testing dependence among serially correlated multicategory variables. Journal of the American Statistical Association 104, 325–337.CrossRefGoogle Scholar
Reeve, Trevor A. and Vigfusson, Robert J. (2011) Evaluating the Forecasting Performance of Commodity Futures Prices. International finance discussion paper no. 1025, Board of Governors of the Federal Reserve System.CrossRefGoogle Scholar
Sanders, Dwight R., Manfredo, Mark R., and Boris, Keith (2008) Evaluating information in multiple horizon forecasts: The DOE's energy price forecasts. Energy Economics 31, 189–196.Google Scholar
Stock, James H. and Watson, Mark W. (2004) Combination forecasts of output growth in a seven-country data set. Journal of Forecasting 23, 405–430.Google Scholar
Strumpf, Dan (2013) Goldman Cuts the Near-Term Brent Crude Forecast to $100 a Barrel. Wall Street Journal, April 23.Google Scholar
Verleger, Philip K. (1982) The determinants of official OPEC crude oil prices. Review of Economics and Statistics 64, 177–183.Google Scholar
Verleger, Philip K. (2011) The margin, currency, and the price of oil. Business Economics 46, 71–82.CrossRefGoogle Scholar