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

  • Neil Shephard
  • Kevin Sheppard

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 analysis 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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Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 438.

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Date of creation: 01 Jul 2009
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Handle: RePEc:oxf:wpaper:438
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