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This recreates ewm(adjust=True).std(): pandas ewm var and std, but I have no luck replicating the calculations in ewm(adjust=False).std(). Replicating ewm(False).mean() is easy but how is the bias corrected when the calculation for variance is recursive? From pandas ewm.std calculation, the right formula seems to be:

var = (1-alpha) * (var_t-1 + bias * alpha * (y-ema_t-1)**2)

where bias=(2-alpha)/(1-alpha)/2. But how does it initialize, as the first values don't match up?

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  • ewm(False).mean() - You mean ewm(adjust=False).mean(), right? Positional arg 1 is com, which is type float. Commented Aug 31 at 12:52
  • Often with rolling windows or lags and leads, datafram.dropna() is used to remove rows such as the first n rows because functions such as ewm leave those rows as NaN. It is a tricky territory Commented Oct 2 at 4:31
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