Which power variation predicts volatility well?
We estimate MIDAS regressions with various (bi)power variations to predict future volatility - measured via increments in quadratic variation. Instead of pre-determining the (bi)power variation we parameterize it and estimate the intra-daily return power transformation that optimally predicts future increments in quadratic variation. We find that the longer the prediction horizon, the smaller the optimal power transformation.
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