How can these residuals have homogeneity of variances? I'm testing the Homogeneity of Variances with Fligner-Killeen test using the function fligner.test in stats
On the chart below I have plotted the residuals that pass this test (p-value > 0.1)

Someone could tell how is it possible that these residuals pass FK test while having a significant movement around the center of the plot?
EDIT:
I'm testing two groups:
A -> res[1:375]
B -> res[376:750]
The residuals are 750, so I'm comparing the two halves.
Thank you
 A: In my opinion, @Alex's questions are right (+1), the FK test originated for the cross-sectional data, not time series objects. Even in subsamples the data points will be dependent, thus the tests are probably not applicable. 
Since heteroscedasticity is a part of some model (you noted these are the residuals, probably from some linear model) you may consider many different alternatives from the lmtest package. 


*

*One of the most common options is the Goldfeld-Quandt test gqtest(), however its ad hoc nature (how you choose to split the samples) makes it less attractive than

*Breusch-Pagan test bptest() the test performs additional regression of squared residuals on the explanatory variables, and in the presence of significant dependence rejects the homoscedastic null

*Another common alternative to the first two tests is a family of White test that are in general presented as LM type of tests comparing original and auxiliary regression models

*If some more complicated structures for the variance of residuals is considered, you may also look for very rich family of conditionally heteroscedastic models.

