Many measures work best in a homoscedastic volatility regime. This is not a big secret. Most regressors, the simplest of which are the ever popular moving averages, are especially biased in the context of a heteroscedastic series.
Probably the best way of normalizing a heteroscedastic series into one with near constant variance is to observe the following. If we assume our process is roughly a SDE with normally distributed innovations (or alternatively a Hurst constant close to 1/2), we know that:
As a rough measure, we can remove much of the vol of vol by scaling our time axis in proportion to the variance. I use a duration based local volatility measure with smoothing or alternatively for daily data an EWMA based evaluation of:
We can then change measure:
where ψ(t) is a smoothing / scaling function. An example of such a scaling (with the red curve in the upper pane indicating the degree of scale from the baseline):