"Technical Analysis in Financial Markets"

Table 2.10: Coefficient estimates EGARCH-model

α φ₁₆ α₀ θ γ β₁

-0.000339 0.066843 -0.194617 0.037536 0.125153 0.976722

(-1.11) (2.49) (-2.83) (2.11) (3.41) (97.58)

Estimates on the daily return series of the LIFFE cocoa futures prices in the period December 12th 1981 until December 31, 1987. The exponential GARCH model is estimated using maximum likelihood using the Marquardt iterative algorithm and Bollerslev-Wooldridge (1992) heteroskedasticity-consistent standard errors and covariance. The numbers within parenthesis are t-ratios.

where m=B (m=S) indicates that we insert a dummy for buy (sell) days, and we will refer to D_m,t as the buy (sell) dummy. Thus D_B,t=1 (D_S,t=1) if day t is a buy (sell) day. For every trading strategy the coefficient for the buy dummy and for the sell dummy are estimated separately. Panel A of table 2.11 shows the percentage of trading rules for which the coefficient of the buy (sell) dummy is significantly positive (negative) at the 10% significance level (second and third column) using a one tailed t-test. The fourth column shows the percentage of trading rules for which the coefficient of the buy dummy is significantly positive and the coefficient of the sell dummy is significantly negative. The results again indicate that the technical trading strategies have forecasting power in the first subperiod. For 40.6% of all trading rules we find that the coefficient of the buy dummy is significantly positive. 27.4% of all trading rules show a significantly negative coefficient of the sell dummy. Finally, 22.8% of all trading rules have a significantly positive coefficient of the buy dummy as well as a significantly negative coefficient of the sell dummy. Panel B of table 2.11 shows that the strategies as a group do not perform statistically badly. For example 1.6% of all trading rules show a significantly negative coefficient of the buy dummy as well as a significantly positive coefficient of the sell dummy. This number is small compared to the 22.8% of the strategies that show statistically significant forecasting power. In comparison with the tests under the assumption of iid returns, it now seems that the trading rules forecast the buy days better than the sell days, while first it was the other way around.