R help - Jan 2004 - How can I test if a not independently and not identicallydistributed time series residuals' are uncorrelated ?

I'm analizing the Argentina stock market (merv)
I  download  the data from yahoo

library(tseries)
Argentina <-
get.hist.quote(instrument="^MERV","1996-10-08","2003-11-03",
quote="Close")

merv <- na.remove(log(Argentina))

I made the Augmented Dickey-Fuller test to analyse
if merv have unit root:
adf.test(merv,k=13)
Dickey-Fuller = -1.4645, p-value = 0.805,
merv have unit root than diff(merv,1) is stationary.

Than I made Breushch-Pagan test to test if residuals are identically
distributed:
library(lmtest)
bptest(merv[2:1730]~-1+merv[1:1729],~merv[1:1729]+I(merv[1:1729])^2)
BP = 81.3443, df = 2, p-value = < 2.2e-16
So merv.reg$resid aren't identically distributed. Than merv is
heteroscedastik.

Finally I made  Box-Ljung test  to test if residuals are independently
distributed:
(H0: merv.reg$resid are independently distributed)
library(ts)
merv.reg <- lm(merv[2:1730]~-1+merv[1:1729])
Box.test(merv.reg$resid, lag=25,type="Ljung")
X-squared = 54.339, df = 25, p-value = 0.0006004
So, there is evidence to REGECT (mistake in laste e-mail)
the null hypothesis,
than the residuals are NOT independently distributed.

Because the residuals are not independently distributed, we know that the
squares of residuals are correlated:
cov[(residuals_t)^2, (residuals_(t-k))^2] <> 0 (not zero for  k <>
0)

But, the residuals could be uncorrelated, (even when they 
are not independent distributed):
cov[residuals_t, residual_(t-k)]=0 !
How can I test that merv.reg$residuals are uncorrelated ?

Thanks a lot.

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