being significant. Without correcting for heteroskedasticity we find for 35 of the 51 indices a significant first order autocorrelation both in local and US currency, while when corrected for heteroskedasticity we find for 30 (23) indices measured in local (US) currency a significant first order autocorrelation at the 10% significance level. It is noteworthy that for more than half of the indices the second order autocorrelation is negative. In contrast, the first order autocorrelation is negative for only 5 (10) indices in local (US) currency. The Ljung-Box (1978) Q-statistics in the second to last columns of tables 5.1 and 5.2 reject for almost all indices the null hypothesis that the first 20 autocorrelations of the returns as a whole are equal to zero. For only 3 (5) indices the null is not rejected in the local (US) currency case, see for example New Zealand's NZSE30 and the Finnish HEX. When looking at the first to last column with Diebold's (1986) heteroskedasticity-consistent Box-Pierce (1970) Q-statistics it appears that heteroskedasticity indeed seriously affects the inferences about serial correlation in the returns. Now for 26 (34) indices the null of no autocorrelation is not rejected in the local (US) currency case. The autocorrelation functions of the squared returns show that for all indices the autocorrelations are high and significant up to order 20. The Ljung-Box (1978) statistics reject the null of no autocorrelation in the squared returns firmly, except for the Venezuela Industrial if expressed in US Dollars. Hence, almost all indices exhibit significant volatility clustering, that is large (small) shocks are likely to be followed by large (small) shocks.

5.3  Empirical results

5.3.1  Results for the mean return criterion

Technical trading rule performance

In section 5.2 we showed that almost half of the local main stock market indices could not even beat a continuous risk free investment. Further we showed that for half of the indices no significant autocorrelation in the daily returns can be found after correction for heteroskedasticity. This implies that there is no linear dependence present in the data. One may thus question whether technical trading strategies can persistently beat the buy-and-hold benchmark. However as noted by Alexander (1961), the dependence in price changes can be of such a complicated nonlinear form that standard linear statistical tools, such as serial correlations, may provide misleading measures of the degree of dependence in the data. Therefore he proposed to use nonlinear technical trading rules to test for dependence. If technical trading rules can capture dependence, which they can profitably trade upon, the question remains whether the profits disappear after implementing
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