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Realising the future: forecasting with high frequency based volatility (HEAVY) models

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  • Neil Shephard

    (Oxford-Man Institute and Department of Economics, University of Oxford)

  • Kevin Sheppard

    (Department of Economics and Oxford-Man Institute, University of Oxford)

Abstract

This paper studies in some detail a class of high frequency based volatility (HEAVY) models. These models are direct models of daily asset return volatility based on realized measures constructed from high frequency data. Our analysis identifies that the models have momentum and mean reversion effects, and that they adjust quickly to structural breaks in the level of the volatility process. We study how to estimate the models and how they perform through the credit crunch, comparing their fit to more traditional GARCH models. We analyse a model based bootstrap which allow us to estimate the entire predictive distribution of returns. We also provide an analysis of missing data in the context of these models.

Suggested Citation

  • Neil Shephard & Kevin Sheppard, 2009. "Realising the future: forecasting with high frequency based volatility (HEAVY) models," Economics Papers 2009-W03, Economics Group, Nuffield College, University of Oxford.
    • Handle: RePEc:nuf:econwp:0903
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    Keywords

    ARCH models; bootstrap; missing data; multiplicative error model; multistep ahead prediction; non-nested likelihood ratio test; realised kernel; realised volatility.;
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