HARQ model: Realised quarticity (Excel)

HARQ is a simple and powerful extension of the heterogeneous autoregressive volatility model (HAR) proposed by Bollerslev et al. (2016). HARQ takes into account the impact of realised variance measurement error that might compomise the validity of HAR coefficients by introducing a realised quarticity term that explicitly estimates the variance of realised variance. Today we are discussing the mathematical and econometric concepts behind the HARQ model, its implementation in Excel with high-frequency data, interpret its results, and discuss its applications for forecasting.
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You can find the spreadsheets for this video and some additional materials here: drive.google.com/drive/folders/1sP40IW0p0w5IETCgo464uhDFfdyR6rh7
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[@jonasbarslund8161]

Thank you for a great video, which really explains the key points of this model! Correct me if I am wrong, but should the interactions terms, the RQ terms, be averages of respectively the weekly and monthly RQ's, just like the RV terms in the original HAR model? At least this is how I read the RQ terms in Bollerslevs paper from 2016. But again thank you got the video! 🙂

[@filipstanciu-ty6ny]

Awesome! Can you please do this model in python? It would be quite helpful

[@NEDLeducation]

Hi Filip, and thanks for the suggestion! I will definitely consider it when I move back into Python tutorials (going to be soon, however I am going through somewhat of an EViews phase now as you might have noticed :) ).