simulations the percentage of strategies with a significantly positive mean buy return (significantly negative mean sell return) is larger than the 26.7% (39.5%) found in the original data series. Hence the autoregressive model seems to explain the good significant results of the technical trading rules as a group for selecting buy and sell days. On the other hand the autoregressive model does not explain the results found for the percentage of strategies with a significantly positive mean buy-sell difference and the percentage of strategies with a significantly positive mean buy as well as a significantly negative mean sell return. Panel B shows again, as in the case of the null of a random walk, that the strategies as a group perform much worse on the simulated autoregressive data series than on the original data series. We can conclude that the autoregressive model neither can explain the good results of the technical trading rules.
Exponential GARCH process
The results of the bootstrap procedure under the null of an exponential GARCH model are similar to those under the null of an autoregressive model. Therefore the good results of the technical trading strategies can also not be explained by the leverage effect, which is accounted for in the exponential GARCH formulation.
Structural break in trend
The last column of table 2.13 lists the bootstrap results of applying the set of trading strategies to simulated autoregressive series with a structural change to account for the different trending behavior of the LIFFE cocoa futures prices. The results change completely in comparison with the other null models. For 41.4% of the simulations the percentage of strategies with a significantly positive mean excess return is larger than the 34.5% found when the same set is applied to the original data series. For 47.8% (52.8%, 24.8%) of the simulations the percentage of strategies with a significantly positive mean buy (significantly negative mean sell, significantly positive mean buy-sell difference) return is larger than the 26.7% (39.5%, 46.7%) found when the same set is applied to the original data series. Even for 42.6% of the simulations the percentage of strategies with a significantly positive mean buy as well as a significantly negative mean sell is larger than the 14.7% found for the original data series. Hence the final model, which allows a structural change, because there is first an upward trend and then a downward trend in the price series, seems to explain the good results found when the set of technical trading strategies is applied to the LIFFE cocoa futures price series in the period 1983:1-1987:12. Probably the trading rules performed well because of the strong trends in the data. Panel
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