English, Article, Journal or magazine article edition: Realising the future: forecasting with high frequency based volatility (HEAVY) models Neil Shephard; Kevin Sheppard

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Bookmark: http://trove.nla.gov.au/version/22657
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Realising the future: forecasting with high frequency based volatility (HEAVY) models
Author
  • Neil Shephard
  • Kevin Sheppard
Physical Description
  • preprint
Notes
  • 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.
  • ARCH models; bootstrap; missing data; multiplicative error model; multistep ahead prediction; non-nested likelihood ratio test; realised kernel; realised volatility.
  • RePEc:sbs:wpsefe:2009fe02
  • 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.
  • ARCH models, Bootstrap, Missing data, Multiplicative error model, Multistep ahead prediction, Non-nested likelihood ratio test, Realised kernal, Realised volatility
  • RePEc:oxf:wpaper:438
Language
  • English
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