Compute the annualized HV of this data: AdjClose 38.24 AdjClose 40.29 AdjClose 41.46 AdjClose 41.52 AdjClose 42.71 AdjClose 39.15 AdjClose 40.32 AdjClose 42.4 AdjClose 41.23 AdjClose 41.43 AdjClose 41.28 AdjClose 42.62 AdjClose 43.21 AdjClose 40.88 AdjClose 37.42 AdjClose 38.44 AdjClose 39.16 AdjClose 40.66 AdjClose 41.16 AdjClose 40.99 AdjClose 42.64 AdjClose 42.18 AdjClose 41.67 AdjClose 42.72 AdjClose 40.9 AdjClose 39.55 AdjClose 40 Now compute the hv of this data, windowed one day ahead: AdjClose 40.29 AdjClose 41.46 AdjClose 41.52 AdjClose 42.71 AdjClose 39.15 AdjClose 40.32 AdjClose 42.4 AdjClose 41.23 AdjClose 41.43 AdjClose 41.28 AdjClose 42.62 AdjClose 43.21 AdjClose 40.88 AdjClose 37.42 AdjClose 38.44 AdjClose 39.16 AdjClose 40.66 AdjClose 41.16 AdjClose 40.99 AdjClose 42.64 AdjClose 42.18 AdjClose 41.67 AdjClose 42.72 AdjClose 40.9 AdjClose 39.55 AdjClose 40 AdjClose 41.58
No need to do the math. If you're doing short term calculations (volatility, simple moving averages, etc.), any time you add a small amount with the new day while taking away a larger amount via the removed day (or vice versa), there's going to be a decent amount of variance in the daily results. If the results are annualized, that further magnifies the differences. That's just the way it is.
because you probably calculated the standard deviation on the prices themselves. Calculate the vols on the returns (log returns) and you will see that the difference boils down to about 0.06% between those 2 different time series.
If not for the AdjClose, I'd would've done the math. But statistics is useless against unknown parms.
yes, about, depending on how you calculate the return measure. Not sure what the problem really is...