What test to see if variances are different when data are non-normal What would be a good test to check if two sample variances are significantly different when data are non normal (leptokurtic, slight negative skew) and heteroskedastic?
The samples are of equal size, 500.
My impression are that most tests assume normality and homoskedasticity.
Edit: I see that my original post was a bit unclear.
I have two samples, where each sample is created by giving different weights to 10 random variables (e.g. one sample has equal weighting of the random variables, 10%, and the other sample have different weights, say 5% for 9 of the variables and 55% for the 10th). All these 10 random variables exhibit heteroskedasticity, and are non normal. The exact weighting of each random variable changes in both samples under a testing period of 500 observations.
I want to test whether or not the sample variance of these two samples are significantly different or not, is there a way to do this?
Could I in some way use Levene's test (or Brown–Forsythe)?
 A: I gather you have 10 random variables that are non-normal and have differing variances.  From these, you want to form two different weighted linear composite variables by combining the original 10 with different weights.  From there, you want to determine if the two different weighted combinations have the same variance.  (Correct me if any of that is wrong.)  
The answer is straightforward: the two resulting variables cannot have the same variance.  For simplicity, let the original variables be independent and consider combining only the first two using $[.3\ \ .3]$ and $[.55\ \ .05]$ as the weights.  Then we can consult the formula for the variance of the weighted sum of two variables:
$$
{\rm Var}(aX + bY) = a^2{\rm Var}(X) + b^2{\rm Var}(Y)
$$
Because you are working with the same variables, ${\rm Var}(X)$ and ${\rm Var}(Y)$ will be the same for both combinations (whatever their individual values are).  So when $a_i\ne a_j$ or $b_i\ne b_j$ (e.g., $.3\ne .55$), then they cannot be the same.  Thus, there is no reason to test them.  
Of course, if you wanted to anyway, you could use the Brown-Forsythe test.  Whether it will be significant will only depend on how much data you have.  
A: May I suggest Conover? Conover two-sample squared ranks test for equality of variance is a non-parametric procedure and not affected by non-normality like Levene's test. Conover squared ranks is implemented in Mathematica and available for MatLab. 
