Application Of Garch Models In Forecasting The Volatility Of Agricultural Commodities

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.