Which power variation predicts volatility well?

Eric Ghysels and Bumjean Sohn

Journal of Empirical Finance, 2009, vol. 16, issue 4, 686-700

Abstract: 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.

Keywords: Stock; Market; Volatility; Forecasting; Power; variation; MIDAS; regressions (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:16:y:2009:i:4:p:686-700

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