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Application Of Garch Models In Forecasting The Volatility Of Agricultural Commodities

Author

Listed:
  • Tony Guida

    (Université de Savoie)

  • Olivier Matringe

    (UNCTAD)

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.

Suggested Citation

  • Tony Guida & Olivier Matringe, 2005. "Application Of Garch Models In Forecasting The Volatility Of Agricultural Commodities," Finance 0512021, EconWPA.
  • Handle: RePEc:wpa:wuwpfi:0512021
    Note: Type of Document - zip; pages: 17
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    References listed on IDEAS

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    1. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    2. 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.
    3. 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.
    4. 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 109-124, April-Jun.
    5. Engle, Robert F & Ng, Victor K, 1993. " Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    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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    More about this item

    Keywords

    GARCH; commodities; volatility; forecasting; risk management;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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