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Forecasting the price of crude oil via convenience yield predictions

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  • Knetsch, Thomas A.

Abstract

The paper develops an oil price forecasting technique which is based on the present value model of rational commodity pricing. The approach suggests shifting the forecasting problem to the marginal convenience yield which can be derived from the cost-of-carry relationship. In a recursive out-of-sample analysis, forecast accuracy at horizons within one year is checked by the root mean squared error as well as the mean error and the frequency of a correct direction-of-change prediction. For all criteria employed, the proposed forecasting tool outperforms the approach of using futures prices as direct predictors of future spot prices. Vis-à-vis the random-walk model, it does not significantly improve forecast accuracy but provides valuable statements on the direction of change.

Suggested Citation

  • Knetsch, Thomas A., 2006. "Forecasting the price of crude oil via convenience yield predictions," Discussion Paper Series 1: Economic Studies 2006,12, Deutsche Bundesbank.
  • Handle: RePEc:zbw:bubdp1:4353
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    Cited by:

    1. Reitz, Stefan & Rülke, Jan & Stadtmann, Georg, 2012. "Nonlinear Expectations in Speculative Markets," Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62045, Verein für Socialpolitik / German Economic Association.
    2. Reitz, Stefan & Rülke, Jan-Christoph & Stadtmann, Georg, 2012. "Nonlinear expectations in speculative markets – Evidence from the ECB survey of professional forecasters," Journal of Economic Dynamics and Control, Elsevier, vol. 36(9), pages 1349-1363.
    3. He, Kaijian & Yu, Lean & Lai, Kin Keung, 2012. "Crude oil price analysis and forecasting using wavelet decomposed ensemble model," Energy, Elsevier, vol. 46(1), pages 564-574.
    4. Reitz Stefan & Rülke Jan-Christoph & Stadtmann Georg, 2010. "Regressive Oil Price Expectations Toward More Fundamental Values of the Oil Price," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 230(4), pages 454-466, August.
    5. 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.
    6. Panopoulou, Ekaterini & Pantelidis, Theologos, 2015. "Speculative behaviour and oil price predictability," Economic Modelling, Elsevier, vol. 47(C), pages 128-136.
    7. 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.
    8. Xiong, Tao & Bao, Yukun & Hu, Zhongyi, 2013. "Beyond one-step-ahead forecasting: Evaluation of alternative multi-step-ahead forecasting models for crude oil prices," Energy Economics, Elsevier, vol. 40(C), pages 405-415.
    9. Julien Chevallier & Benoît Sévi, 2013. "A Fear Index to Predict Oil Futures Returns," Working Papers 2013.62, Fondazione Eni Enrico Mattei.
    10. Emanuele De Meo, 2013. "Are Commodity Prices Driven by Fundamentals?," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 42(1), pages 19-46, February.
    11. Reitz, Stefan & Rülke, Jan-Christoph & Stadtmann, Georg, 2009. "Are oil price forecasters finally right? Regressive expectations toward more fundamental values of the oil price," Discussion Paper Series 1: Economic Studies 2009,32, Deutsche Bundesbank.
    12. Slabá, Monika & Gapko, Petr & Klimešová, Andrea, 2013. "Main drivers of natural gas prices in the Czech Republic after the market liberalisation," Energy Policy, Elsevier, vol. 52(C), pages 199-212.
    13. repec:dau:papers:123456789/11714 is not listed on IDEAS
    14. Alquist, Ron & Kilian, Lutz & Vigfusson, Robert J., 2013. "Forecasting the Price of Oil," Handbook of Economic Forecasting, Elsevier.
    15. Kuper, Gerard H., 2012. "Inventories and upstream gasoline price dynamics," Energy Economics, Elsevier, vol. 34(1), pages 208-214.
    16. Naser, Hanan, 2016. "Estimating and forecasting the real prices of crude oil: A data rich model using a dynamic model averaging (DMA) approach," Energy Economics, Elsevier, vol. 56(C), pages 75-87.
    17. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil prices," MPRA Paper 77531, University Library of Munich, Germany.
    18. Carlos Caceres & Leandro Medina, 2012. "Measures of Fiscal Risk in Hydrocarbon-Exporting Countries," IMF Working Papers 12/260, International Monetary Fund.
    19. An, Haizhong & Gao, Xiangyun & Fang, Wei & Ding, Yinghui & Zhong, Weiqiong, 2014. "Research on patterns in the fluctuation of the co-movement between crude oil futures and spot prices: A complex network approach," Applied Energy, Elsevier, vol. 136(C), pages 1067-1075.
    20. repec:dau:papers:123456789/11663 is not listed on IDEAS
    21. Chau, Frankie & Kuo, Jing-Ming & Shi, Yukun, 2015. "Arbitrage opportunities and feedback trading in emissions and energy markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 36(C), pages 130-147.
    22. Ai Han & Yanan He & Yongmiao Hong & Shouyang Wang, 2013. "Forecasting Interval-valued Crude Oil Prices via Autoregressive Conditional Interval Models," WISE Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.

    More about this item

    Keywords

    oil price forecasts; rational commodity pricing; convenience yield; single-equation model;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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