Try straight Box-Cox transform as per Box, G. E. P. and Cox, D. R. (1964), "An Analysis of Transformations," Journal of the Royal Statistical Society, Series B, 26, 211--234. SAS has the description of its loglikelihood function in Normalizing Transformations, which you can use to find the optimal $\lambda$ parameter, which is described in Atkinson, A. C. (1985), Plots, Transformations, and Regression, New York: Oxford University Press.
It's very easy to implement it having the LL function, or if you have a stat package like SAS or MATLAB use their commands: it's boxcox command in MATLAB and PROC TRANSREG in SAS.
Also, in R this is in the MASS package, function boxcox().
Kurtosis is really pretty simple ... and useful. It is simply a measure of outliers, or tails. It has nothing to do with the peak whatsoever - that definition must be abandoned.
Here is a data set:
0, 3, 4, 1, 2, 3, 0, 2, 1, 3, 2, 0, 2, 2, 3, 2, 5, 2, 3, 999
Notice that '999' is an outlier.
Here are the $z^4$ values from the data set:
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 360.98
Notice that only the outlier gives a $z^4$ that is noticeably different from 0.
The average of these $z^4$ values is the kurtosis of the empirical distribution (subtract 3 if you like, it doesn't matter for the point I am making): 18.05
It should be obvious from this calculation that the data near the "peak" (the non-outlier data) contribute almost nothing to the kurtosis statistic.
Kurtosis is useful as a measure of outliers. Outliers are important to elementary students and therefore kurtosis should be taught. But kurtosis has virtually nothing to do with the peak, whether it is pointy, flat, bimodal or infinite. You can have all the above with small kurtosis and all of the above with large kurtosis. So it should NEVER be presented as having anything to do with the peak, because that will be teaching incorrect information. It also makes the material needless confusing, and seemingly less useful.
Summary:
- kurtosis is useful as a measures of tails (outliers).
- kurtosis has nothing to do with the peak.
- kurtosis is practically useful and should be taught, but only as a measure of outliers. Do not mention peak when teaching kurtosis.
This article explains clearly why the "Peakedness" definition is now officially dead.
Westfall, P.H. (2014). "Kurtosis as Peakedness, 1905 – 2014. R.I.P." The American Statistician, 68(3), 191–195.
Best Answer
I would imagine the DCC suffers the same limitations as the regular correlation with non-normal data. That is, there isn't an assumption of normality, but non-normal data can cause odd findings; see the Anscombe quartet, for example.
As for kurtosis, taking the log can certainly make it worse. Take this example of the uniform distribution:
where a Normally distributed variable has kurtosis of 3.
on the other hand, in this example
However, you mention skewed data with kurtosis. Was your data right skew or left skew? Since the former is more common, I'll guess that.
Here, taking the log improves kurtosis and skewness.
Taking the log had almost no effect on kurtosis.
As always, try plotting the data to see what is going on in your correlation.