IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/84009.html
   My bibliography  Save this paper

On the difficulty of interpreting market behaviour in an uncertain world: the case of oil futures pricing between 2003 and 2016

Author

Listed:
  • Cifarelli, Giulio
  • Paesani, Paolo

Abstract

Our results show that over the two cycles that characterize the 2003-2016 period a significant change in the working of oil markets occurs. Our pricing investigation, based on a three-agent model (hedgers, fundamentalist speculators and chartists), find that from 2009 onwards traditional analysis of supply and demand forecasts, loses its explanatory power and hence its credibility. The sharp and unexpected fluctuations in oil prices, compounded by unpredictable political factors and technological break-troughs (e.g. tight sands/shale oil) strongly raises uncertainty and reduces the effectiveness of customary forecasting techniques.

Suggested Citation

  • Cifarelli, Giulio & Paesani, Paolo, 2017. "On the difficulty of interpreting market behaviour in an uncertain world: the case of oil futures pricing between 2003 and 2016," MPRA Paper 84009, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:84009
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/84009/1/MPRA_paper_84009.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Stefan Reitz & Frank Westerhoff, 2007. "Commodity price cycles and heterogeneous speculators: a STAR–GARCH model," Empirical Economics, Springer, vol. 33(2), pages 231-244, September.
    2. repec:bla:ecorec:v:0:y:1986:i:0:p:24-38 is not listed on IDEAS
    3. Vo, Minh T., 2009. "Regime-switching stochastic volatility: Evidence from the crude oil market," Energy Economics, Elsevier, vol. 31(5), pages 779-788, September.
    4. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    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. James D. Hamilton, 2009. "Understanding Crude Oil Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 179-206.
    7. Daniel Santabárbara, 2017. "The oil market: recent developments and outlook," Economic Bulletin, Banco de España, issue SEP.
    8. Roberto Dieci & Xue-Zhong He & Cars Hommes (ed.), 2014. "Nonlinear Economic Dynamics and Financial Modelling," Springer Books, Springer, edition 127, number 978-3-319-07470-2, February.
    9. Ke Tang & Wei Xiong, 2012. "Index Investment and the Financialization of Commodities," Financial Analysts Journal, Taylor & Francis Journals, vol. 68(6), pages 54-74, November.
    10. Figuerola-Ferretti, Isabel & Gonzalo, Jesús, 2010. "Modelling and measuring price discovery in commodity markets," Journal of Econometrics, Elsevier, vol. 158(1), pages 95-107, September.
    11. Silvennoinen, Annastiina & Thorp, Susan, 2013. "Financialization, crisis and commodity correlation dynamics," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 24(C), pages 42-65.
    12. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
    13. Westerhoff, Frank H., 2004. "Multiasset Market Dynamics," Macroeconomic Dynamics, Cambridge University Press, vol. 8(5), pages 596-616, November.
    14. Westerhoff, Frank & Reitz, Stefan, 2005. "Commodity price dynamics and the nonlinear market impact of technical traders: empirical evidence for the US corn market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 349(3), pages 641-648.
    15. Tokic, Damir, 2011. "Rational destabilizing speculation, positive feedback trading, and the oil bubble of 2008," Energy Policy, Elsevier, vol. 39(4), pages 2051-2061, April.
    16. Cifarelli, Giulio, 2013. "Smooth transition regime shifts and oil price dynamics," Energy Economics, Elsevier, vol. 38(C), pages 160-167.
    17. Wan, Jer-Yuh & Kao, Chung-Wei, 2009. "Evidence on the contrarian trading in foreign exchange markets," Economic Modelling, Elsevier, vol. 26(6), pages 1420-1431, November.
    18. Nakov, Anton & Nuño, Galo, 2011. "Saudi Aramco and the oil market," Working Paper Series 1354, European Central Bank.
    19. Shleifer, Andrei & Vishny, Robert W, 1997. "The Limits of Arbitrage," Journal of Finance, American Finance Association, vol. 52(1), pages 35-55, March.
    20. Cifarelli, Giulio & Paladino, Giovanna, 2011. "Hedging vs. speculative pressures on commodity futures returns," MPRA Paper 28229, University Library of Munich, Germany.
    21. Morana, Claudio, 2001. "A semiparametric approach to short-term oil price forecasting," Energy Economics, Elsevier, vol. 23(3), pages 325-338, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Giulio Cifarelli and Paolo Paesani, 2021. "Navigating the Oil Bubble: A Non-linear Heterogeneous-agent Dynamic Model of Futures Oil Pricing," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    2. Baur, Dirk G. & Glover, Kristoffer J., 2014. "Heterogeneous expectations in the gold market: Specification and estimation," Journal of Economic Dynamics and Control, Elsevier, vol. 40(C), pages 116-133.
    3. Joëts, Marc, 2015. "Heterogeneous beliefs, regret, and uncertainty: The role of speculation in energy price dynamics," European Journal of Operational Research, Elsevier, vol. 247(1), pages 204-215.
    4. Cifarelli, Giulio & Paladino, Giovanna, 2018. "Can the interaction between a single long-term attractor and heterogeneous trading explain exchange rate behaviour? A nonlinear econometric investigation," MPRA Paper 83894, University Library of Munich, Germany.
    5. Cifarelli, Giulio & Paladino, Giovanna, 2018. "Can the interaction between a single long-term attractor and heterogeneous trading explain the exchange rate conundrum?," Research in International Business and Finance, Elsevier, vol. 46(C), pages 313-323.
    6. Frijns, Bart & Lehnert, Thorsten & Zwinkels, Remco C.J., 2010. "Behavioral heterogeneity in the option market," Journal of Economic Dynamics and Control, Elsevier, vol. 34(11), pages 2273-2287, November.
    7. ter Ellen, Saskia & Hommes, Cars H. & Zwinkels, Remco C.J., 2021. "Comparing behavioural heterogeneity across asset classes," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 747-769.
    8. Saskia ter Ellen & Willem F. C. Verschoor, 2018. "Heterogeneous Beliefs and Asset Price Dynamics: A Survey of Recent Evidence," Dynamic Modeling and Econometrics in Economics and Finance, in: Fredj Jawadi (ed.), Uncertainty, Expectations and Asset Price Dynamics, pages 53-79, Springer.
    9. Stephanie-Carolin Grosche, 2014. "What Does Granger Causality Prove? A Critical Examination of the Interpretation of Granger Causality Results on Price Effects of Index Trading in Agricultural Commodity Markets," Journal of Agricultural Economics, Wiley Blackwell, vol. 65(2), pages 279-302, June.
    10. Cars Hommes & Florian Wagener, 2008. "Complex Evolutionary Systems in Behavioral Finance," Tinbergen Institute Discussion Papers 08-054/1, Tinbergen Institute.
    11. Scott H. Irwin & Dwight R. Sanders, 2011. "Index Funds, Financialization, and Commodity Futures Markets," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 33(1), pages 1-31.
    12. Ing-Haw Cheng & Wei Xiong, 2014. "Financialization of Commodity Markets," Annual Review of Financial Economics, Annual Reviews, vol. 6(1), pages 419-441, December.
    13. Sophie van Huellen, 2013. "Price Non-Convergence in Commodities: A Case Study of the Wheat Conundrum," Working Papers 185, Department of Economics, SOAS University of London, UK.
    14. Ellen, Saskia ter & Zwinkels, Remco C.J., 2010. "Oil price dynamics: A behavioral finance approach with heterogeneous agents," Energy Economics, Elsevier, vol. 32(6), pages 1427-1434, November.
    15. Cifarelli, Giulio, 2013. "Smooth transition regime shifts and oil price dynamics," Energy Economics, Elsevier, vol. 38(C), pages 160-167.
    16. Kukacka, Jiri & Barunik, Jozef, 2017. "Estimation of financial agent-based models with simulated maximum likelihood," Journal of Economic Dynamics and Control, Elsevier, vol. 85(C), pages 21-45.
    17. Frijns, Bart & Zwinkels, Remco C.J., 2020. "Absence of speculation in the European sovereign debt markets," Journal of Economic Behavior & Organization, Elsevier, vol. 169(C), pages 245-265.
    18. Giulio Cifarelli, 2023. "Commodity Pricing Volatility Shifts in a Highly Turbulent Time Period. A Time-varying Transition Probability Markov Switching Analysis," Working Papers - Economics wp2023_11.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    19. Chen, Yu-Lun & Mo, Wan-Shin & Chang, Ya-Kai, 2022. "Investor sentiment spillover effect and market quality in crude oil futures," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 177-193.
    20. Du, Xiaodong & Yu, Cindy L. & Hayes, Dermot J., 2011. "Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis," Energy Economics, Elsevier, vol. 33(3), pages 497-503, May.

    More about this item

    Keywords

    Oil pricing; Speculation; Dynamic hedging; Logistic smooth transition; Multivariate GARCH;
    All these keywords.

    JEL classification:

    • F2 - International Economics - - International Factor Movements and International Business
    • F30 - International Economics - - International Finance - - - General
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • G1 - Financial Economics - - General Financial Markets
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:84009. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.