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Which power variation predicts volatility well?

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Author Info
Ghysels, Eric
Sohn, Bumjean
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.

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File URL: http://www.sciencedirect.com/science/article/B6VFG-4W04KG5-1/2/89e00ab5b3059eb6dd4d4225acb7f5f7
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Publisher Info
Article provided by Elsevier in its journal Journal of Empirical Finance.

Volume (Year): 16 (2009)
Issue (Month): 4 (September)
Pages: 686-700
Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Handle: RePEc:eee:empfin:v:16:y:2009:i:4:p:686-700

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Web page: http://www.elsevier.com/locate/jempfin

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Related research
Keywords: Stock Market Volatility Forecasting Power variation MIDAS regressions;

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This page was last updated on 2009-12-30.

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