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

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  • Neil Shephard

    ()

  • Kevin Sheppard

    ()

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.

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Bibliographic Info

Paper provided by Oxford Financial Research Centre in its series OFRC Working Papers Series with number 2009fe02.

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Length: 41
Date of creation: 2009
Date of revision:
Handle: RePEc:sbs:wpsefe:2009fe02

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Web page: http://www.finance.ox.ac.uk
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Related research

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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References

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