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Application Of Garch Models In Forecasting The Volatility Of Agricultural Commodities

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Author Info

  • Tony Guida

    (Université de Savoie)

  • Olivier Matringe

    (UNCTAD)

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    Abstract

    This paper examines the forecasting performance of GARCH’s models used with agricultural commodities data. We compare different possible sources of forecasting improvement, using various statistical distributions and models. We have chosen to confine our analysis on four indices which are the cocoa LIFFE continuous futures, the cocoa NYBOT continuous futures, the coffee NYBOT continuous futures and the CAC 40, the French major stock index. As one may see the sample of indices is containing a genuine stock index also. The implied goal is to find out if the GARCH models are more fitted for stock indices than for agricultural commodities. The forecasts and the predictive power are evaluated using traditional methods such as the coefficient of determination in the regression of the true variance on the predicted one. We find that agricultural commodities time series could not be used with the same methodology than the financial series. Moreover it is interesting to point out that no real “model leader” was found in this sample of commodities. Finally increased forecast performance is not solely observed using non-gaussian distribution in commodities.

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    File URL: http://128.118.178.162/eps/fin/papers/0512/0512021.zip
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    Bibliographic Info

    Paper provided by EconWPA in its series Finance with number 0512021.

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    Length: 17 pages
    Date of creation: 20 Dec 2005
    Date of revision:
    Handle: RePEc:wpa:wuwpfi:0512021

    Note: Type of Document - zip; pages: 17
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    Web page: http://128.118.178.162

    Related research

    Keywords: GARCH; commodities; volatility; forecasting; risk management;

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    1. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
    2. Robert F. Engle & Victor K. Ng, 1991. "Measuring and Testing the Impact of News on Volatility," NBER Working Papers 3681, National Bureau of Economic Research, Inc.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
    5. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
    6. Baillie, Richard T & Myers, Robert J, 1991. "Bivariate GARCH Estimation of the Optimal Commodity Futures Hedge," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(2), pages 109-24, April-Jun.
    7. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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