Suppose we have some financial time series. When we calculate the standard ACF, $\mu$ is considered as the average of all series' values. However, if we have a volatile series, the average can be biased towards high prices that were long time ago and, intuitively, "local" average (for example, the average of prices during current week) seems to be more relevant to calculate autocorrelations.
Can you please explain me, am I right given that I try to predict next day return? Why is calculating ACF on the whole series is considered more standard (at least in textbooks) than the rolling one?