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Currently I use the EWMA model with the squared logarithmic returns as proxy estimator for the volatility, in order to forecast the volatility one step ahead in an intraday scenario (time frame is a couple of minutes)

However I read and observed the squared returns as volatility estimator has its limitations. So now I want to use a more sophisticated estimator such as Garman-Klass.

My question is:

  • Is possible and moreover sensible to combine an estimator such as Garman-Klass with a volatility model like EWMA or any of the ARCH family?
  • Or do I even need a volatility model like EWMA or *ARCH when I use these estimator (i.e Garman-Klass) in order to forecast the volatility ?
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There are papers about improving *ARCH models by using other estimators than the classic squared returns estimator.

Here are some links:

In the paper How Useful are the Various Volatility Estimators for Improving GARCH-based Volatility Forecasts? Evidence from the Nasdaq - 100 Stock Index for example the researches compare different estimators such as Parkinson, Garman-Klass used in the GARCH model. They provide a measure of how useful these estimators are, by comparing them with different error measures.

In A Range-Based GARCH Model for Forecasting Volatility by Dennis S. Mapa different GARCH models such as Garch(1,2) EGarch and the like are upgraded by using the Parkinson estimator. And then again they are all compared by different error measures.

Both were very interesting reads for me.

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  • $\begingroup$ Could you also include titles of the papers (in case the links would go dead at some point in the future)? $\endgroup$ – Richard Hardy Oct 7 '16 at 13:58

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