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A General Approach to Recovering Market Expectations from Futures Prices with an Application to Crude Oil

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  • Christiane Baumeister
  • Lutz Kilian

Abstract

Futures markets are a potentially valuable source of information about price expectations. Exploiting this information has proved difficult in practice, because time-varying risk premia often render the futures price a poor measure of the market expectation of the price of the underlying asset. Although this expectation in principle may be recovered by adjusting the futures price by the estimated risk premium, a common problem is that there are as many measures of the market expectation as there are estimates of the risk premium. We propose a general solution to this problem that allows us to select the most accurate estimate of the expectation for any set of risk premium estimates. We illustrate this approach by solving the long-standing problem of how to estimate the market expectation of the price of crude oil. We provide a new measure of oil price expectations that is substantially more accurate than the alternatives and more economically plausible. Our analysis has implications for the estimation of economic models of energy-intensive durables, for oil price forecasting and for the measurement of oil price shocks.

Suggested Citation

  • Christiane Baumeister & Lutz Kilian, 2016. "A General Approach to Recovering Market Expectations from Futures Prices with an Application to Crude Oil," Staff Working Papers 16-18, Bank of Canada.
  • Handle: RePEc:bca:bocawp:16-18
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    References listed on IDEAS

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    Cited by:

    1. Sung Je Byun, 2017. "Speculation in Commodity Futures Markets, Inventories and the Price of Crude Oil," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    2. repec:eee:pubeco:v:161:y:2018:i:c:p:1-14 is not listed on IDEAS
    3. Balcılar, Mehmet & Demirer, Rıza & Hammoudeh, Shawkat, 2015. "Regional and global spillovers and diversification opportunities in the GCC equity sectors," Emerging Markets Review, Elsevier, vol. 24(C), pages 160-187.
    4. repec:eee:energy:v:139:y:2017:i:c:p:617-629 is not listed on IDEAS
    5. Christiane Baumeister & Lutz Kilian, 2016. "Understanding the Decline in the Price of Oil since June 2014," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 3(1), pages 131-158.
    6. Gambetti, Luca & Moretti, Laura, 2017. "News, Noise and Oil Price Swings," Research Technical Papers 12/RT/17, Central Bank of Ireland.
    7. repec:eee:inecon:v:108:y:2017:i:c:p:368-376 is not listed on IDEAS
    8. De Rezende, Rafael B., 2017. "The interest rate effects of government bond purchases away from the lower bound," Journal of International Money and Finance, Elsevier, vol. 74(C), pages 165-186.
    9. Arnold McIntyre & Ahmed El-Ashram & Marcio Ronci & Julien Reynaud & Natasha X Che & Ke Wang & Sebastian Acevedo Mejia & Mark Scott Lutz, 2016. "Caribbean Energy; Macro-Related Challenges," IMF Working Papers 16/53, International Monetary Fund.
    10. Jorge Toro & Aarón Garavito & David Camilo López & Enrique Montes, 2015. "El choque petrolero y sus implicaciones en la economía colombiana," BORRADORES DE ECONOMIA 013829, BANCO DE LA REPÚBLICA.
    11. Bielen, David A. & Newell, Richard G. & Pizer, William A., 2018. "Who did the ethanol tax credit benefit? An event analysis of subsidy incidence," Journal of Public Economics, Elsevier, vol. 161(C), pages 1-14.
    12. Reinhard Ellwanger, 2017. "On the Tail Risk Premium in the Oil Market," Staff Working Papers 17-46, Bank of Canada.
    13. Jacopo Piana & Daniele Bianchi, 2017. "Expected Spot Prices and the Dynamics of Commodity Risk Premia," 2017 Meeting Papers 1149, Society for Economic Dynamics.
    14. Richard G. Newell & Brian C. Prest, 2017. "The Unconventional Oil Supply Boom: Aggregate Price Response from Microdata," NBER Working Papers 23973, National Bureau of Economic Research, Inc.

    More about this item

    Keywords

    Econometric and statistical methods; International topics;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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