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

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  • Anning Wei

    (Rabobank, Hong Kong)

  • Raymond M. Leuthold

    (University of Illinois at Urbana-Champaign)

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    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.

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    File URL: http://128.118.178.162/eps/fin/papers/9805/9805001.pdf
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    Bibliographic Info

    Paper provided by EconWPA in its series Finance with number 9805001.

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    Length: 56 pages
    Date of creation: 04 May 1998
    Date of revision:
    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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    Keywords: futures markets; ARCH; chaos;

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    Cited by:
    1. 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), issue 1, April.
    2. Brunetti, Celso & Gilbert, Christopher L., 2000. "Bivariate FIGARCH and fractional cointegration," Journal of Empirical Finance, Elsevier, vol. 7(5), pages 509-530, December.
    3. Roberto ESPOSTI & Giulia LISTORTI, 2011. "Agricultural Price Transmission Across Space and Commodities During Price Bubbles," Working Papers 367, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    4. 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.
    5. Guillermo Benavides, 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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