# Why are there different estimators for stock volatility? (realized variance, RAV, etc)

I am very confused about why different volatility estimators (RV, RAV, BPV, etc) exist. If the goal is to find the best estimator for stock volatility, and volatility is latent, how do I know which estimator performs the best when the mathematical definition for these estimators are all different?

Realized variance = sum[(return at t)^2] in a day
Realized absolute value = sum|return at t| in a day
Bipower variation = sum |return at t-1|*|return at t| in a day


If I use a linear model and substitute each of these estimators as the regressor, doesn't this mean I can't compare the results directly, but I can only compare how well the linear model is able to predict itself based on MSE, R^2, and so on?

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