GARCH model - volatility persistence in time series (Excel)

Generalised autoregressive conditional hereroskedasticity (GARCH) is an extension over ARCH that has been proposed by Tim Bollerslev in 1986. It allows for even more persistent volatility and is extremely useful, especially in high-frequency financial and economic time series. Today we will learn how to apply it in Excel and how to interpret its results. Econometrics is easy with NEDL!

[iceman]

Never have found such good explanations and excel templates for the Finance world. Good job!

[rjmorpheus]

I wish I'd had these videos during my time in University. it would have made my life a whole lot easier.
Subbed!!!!

[vinucharles]

Well explained the complex model in a short time

[nilanjanachakraborty]

Very helpful presentation. Clearly and systematically explained.

[KenBeans]

U sound like a god to me. I cant imagine how long you have been doing this

[mgk]

I take my hat off, you're the boss.🤝

[sunday-thequant]

hey bro! i learn a lot from your videos! thanks!

[MlleNnCo]

Many thanks, I really needed these clear explanations to tackle a GARCH model project with R.

[RonaldRKumar]

Thanks. Very helpful. Looking forward to seeing more video's from you. Best regards :)

[aravindganesan]

Thanks for this. Look forward to the video on standard error estimation for ARCH and GARCH models.

[kergok]

best explanation. thanks a lot. your videos are so helpful

[ghcmartins]

I´ll be doing a Patreon donation, you totally deserve it! Great content explained in short time!

[Paragraf]

What would be very interesting to see you make is a model that predicts/forecasts volatility in the future of a portfolio/stock/index based on historical data? Preferably a forecast based on cyclical nature and not linear, as most financial instruments trade in a cyclical manner and not linear.

[ZahidRahimov]

Thanks a lot for interesting and useful video

[mohammadtayeh]

Brilliant, ,, very good one, ,, I like it so much thanks a lot

[張謙-wt]

Great videos! many thanks looking forward to your videos on the standard errors on alpha and beta. Hope it will be coming soon...

[RenormalizedAdvait]

This is basically a GARCH(1, 1) process with one, one lag, for a general GARCH(p, q) one must resort to ACF and PACF plots.

[jesper]

Hi NEDL, I have done the GARCH model exactly as you did it in this video, but Ive done it on my own data set. Now I have a problem, I need to get the Normalized Returns because there was heteroskedasticity in my data set. I need to do an OLS regression with those Normalized Returns. How do I get the Normalized Returns when I have performed the GARCH just like you did it in this video. Could you please help me?

[letmefizzle]

Thank you for the explanation! I was wondering if it is possible to add an independent variable to the GARCH(1, 1) model. A lot of researches only look at the volatility of a stock, but is it also possible to add an independent variable (for example media sentiment) to see if that also influences volatility? And if this is possible, what would the GARCH-model formula look like. Thank you in advance and keep up the great work!

[alem_mood]

Great Video! Clever and professional!
Are you planning as next steps ARMA - ARIMA models and ARIMA - GARCH model?
Would be greatly appreciated!