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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 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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Bibliographic Info

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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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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