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Long Agricultural Futures Prices: ARCH, Long Memory, or Chaos Processes?

Author

Listed:
  • Anning Wei

    (Rabobank, Hong Kong)

  • Raymond M. Leuthold

    (University of Illinois at Urbana-Champaign)

Abstract

Price series that are 21.5 years long for six agricultural futures markets, corn, soybeans, wheat, hogs, coffee and sugar, possess characteristics consistent with nonlinear dynamics. Three nonlinear models, ARCH, long memory and chaos, are able to produce these symptoms. Using daily, weekly and monthly data for the six markets, each of these models is tested against the martingale difference null, one-by-one. Standard ARCH tests suggest that all series might contain ARCH effects, but further diagnostics show that the series are not ARCH processes, failing to reject the null. A long-memory technique, the AFIMA model, fails to find long-memory structures in the data, except for sugar. This allows chaos analysis to be applied directly to the raw data. Carefully specifying phase space, and utilizing correlation dimension and Lyapunov exponent together, the remaining five price series are found to be chaotic processes.

Suggested Citation

  • Anning Wei & Raymond M. Leuthold, 1998. "Long Agricultural Futures Prices: ARCH, Long Memory, or Chaos Processes?," Finance 9805001, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpfi:9805001
    Note: Type of Document - pdf; prepared on PC; to print on HP Laserjet; pages: 56; figures: included. Office for Futures and Options Research (OFOR) at the University of Illinois at Urbana-Champaign. Working Paper 98-03. For a complete list of OFOR working papers see
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/fin/papers/9805/9805001.pdf
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    References listed on IDEAS

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    Cited by:

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    2. Listorti, Giulia & Esposti, Roberto, 2012. "Horizontal Price Transmission in Agricultural Markets: Fundamental Concepts and Open Empirical Issues," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 1(1), pages 1-28, April.
    3. Benavides Guillermo, 2010. "Forecasting Short-Run Inflation Volatility using Futures Prices: An Empirical Analysis from a Value at Risk Perspective," Working Papers 2010-12, Banco de México.
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    5. Sophie Mitra & Jean-Marc Boussard, 2008. "Storage and the Volatility of Agricultural Prices: A Model of Endogenous Fluctuations," Fordham Economics Discussion Paper Series dp2008-11, Fordham University, Department of Economics.
    6. Guillermo Benavides, 2021. "Asymmetric Volatility Relevance in Risk Management: An Empirical Analysis using Stock Index Futures," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(TNEA), pages 1-18, Septiembr.
    7. Esposti, Roberto, 2021. "On the long-term common movement of resource and commodity prices.A methodological proposal," Resources Policy, Elsevier, vol. 72(C).
    8. Orzeszko, Witold, 2008. "The new method of measuring the effects of noise reduction in chaotic data," Chaos, Solitons & Fractals, Elsevier, vol. 38(5), pages 1355-1368.
    9. Marisa Faggini, 2011. "Chaotic Time Series Analysis in Economics: Balance and Perspectives," Working papers 25, Former Department of Economics and Public Finance "G. Prato", University of Torino.
    10. Akhmet, Marat & Akhmetova, Zhanar & Fen, Mehmet Onur, 2014. "Chaos in economic models with exogenous shocks," Journal of Economic Behavior & Organization, Elsevier, vol. 106(C), pages 95-108.
    11. Belhajjam, A. & Belbachir, M. & El Ouardirhi, S., 2017. "Robust multivairiate extreme value at risk allocation," Finance Research Letters, Elsevier, vol. 23(C), pages 1-11.
    12. Marisa Faggini & Bruna Bruno & Anna Parziale, 2019. "Does Chaos Matter in Financial Time Series Analysis?," International Journal of Economics and Financial Issues, Econjournals, vol. 9(4), pages 18-24.
    13. Martin Odening & Oliver Mußhoff & Alfons Balmann, 2005. "Investment decisions in hog finishing: an application of the real options approach," Agricultural Economics, International Association of Agricultural Economists, vol. 32(1), pages 47-60, January.
    14. John Francis Diaz & Jo-Hui Chen, 2017. "Testing for Long-memory and Chaos in the Returns of Currency Exchange-traded Notes (ETNs)," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 7(4), pages 1-2.
    15. Guillermo Benavides, 2010. "Forecasting Short-Run Inflation Volatility using Futures Prices: An Empirical Analysis from a Value at Risk Perspective," Revista de Administración, Finanzas y Economía (Journal of Management, Finance and Economics), Tecnológico de Monterrey, Campus Ciudad de México, vol. 4(2), pages 1-27.
    16. Roberto Esposti & Giulia Listorti, 2013. "Agricultural price transmission across space and commodities during price bubbles," Agricultural Economics, International Association of Agricultural Economists, vol. 44(1), pages 125-139, January.
    17. Yankou Diasso, 2014. "Dynamique du prix international du coton : aléas, aversion au risque et chaos," Recherches économiques de Louvain, De Boeck Université, vol. 80(4), pages 53-86.
    18. Esposti, Roberto, 2017. "What Makes Commodity Prices Move Together? An Answer From A Dynamic Factor Model," 2017 International Congress, August 28-September 1, 2017, Parma, Italy 260889, European Association of Agricultural Economists.

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    More about this item

    Keywords

    futures markets; ARCH; chaos;
    All these keywords.

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics

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