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Which power variation predicts volatility well?

  • Ghysels, Eric
  • Sohn, Bumjean
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    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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    Article provided by Elsevier in its journal Journal of Empirical Finance.

    Volume (Year): 16 (2009)
    Issue (Month): 4 (September)
    Pages: 686-700

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    Handle: RePEc:eee:empfin:v:16:y:2009:i:4:p:686-700
    Contact details of provider: Web page: http://www.elsevier.com/locate/jempfin

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    1. Asger Lunde & Peter Reinhard Hansen, 2001. "A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?," Working Papers 2001-04, Brown University, Department of Economics.
    2. Ole E. Barndorff-Nielsen, 2004. "Power and Bipower Variation with Stochastic Volatility and Jumps," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(1), pages 1-37.
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    8. Andrew Patton, 2006. "Volatility Forecast Comparison using Imperfect Volatility Proxies," Research Paper Series 175, Quantitative Finance Research Centre, University of Technology, Sydney.
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