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I'm in the process of constructing volatility cones for several assets and I want to make sure the data is free of biases.

I know that using overlapping data introduces an artificial degree of correlation between samples and I have the formula for adjustment however I'm unsure how to apply it to the data set.

I'm not necessarily concerned with looking at the mean and variance of the entire data set as I am in looking at the absolute values (min, max, 90th percentile, etc)

I'm wondering if each sample (i.e. 30 day window) needs to be adjusted or whether the bias only applied to the variance of entire data set (the variance of variance of overlapping returns)

Any clarification is greatly appreciated. Thank you very much

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You ask how to apply "the formula" but you don't reference to it .... could you please? I am very curious ... –  Richard Apr 28 at 7:12
    
sure the formula is: m = 1/(1-(h/n)+((h^2-1)/3n^2)) where h is the length of each subseries (i.e. 20 days) and n is the number of distinct subseries available for the total number of observations T.. so n = T - h + 1 –  user7920 Apr 28 at 14:23

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