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I'm working on a project to forecast volatility and I'm using intraday data (1 min). I want to include exogenous variables to the model that have daily frequency. I was wondering if GARCH-MIDAS can be used for this? The papers I have read on this model use daily price data and the R-package description (mfGARCH) also says

[...] The GARC-HMIDAS model decomposes the conditional variance of (daily) stock returns into a shortand long-term component, where the latter may depend on an exogenous covariate sampled at a lower frequency.

Thanks!

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I guess its possible if you employ some kind of GARCH with an intraday component. In general, it should not be too difficult to alter my R-package mfGARCH for estimating it. Maybe

http://www.unstarched.net/2013/03/20/high-frequency-garch-the-multiplicative-component-garch-mcsgarch-model/

could be a start for modeling intraday seasonality.

Best, Onno

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It is a good idea indeed to use GARCH for intraday volatility because it is as clustered as daily volatility. Moreover, if you want to account for autocorrelations, you should consider using other variables like the bid-ask spread, the traded volume and the volume of the book at first limits. It is done in Endogeneous Dynamics of Intraday Liquidity by Binkowski and L (2018). It is shown that if you add traded volumes it will improve the modeling of volatility (especially for US stocks) and you can gain a little more if you add the 2 other variables (see the figure).

enter image description here

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