Wached mit 1 hour video and couldn't understand the concept and you just explained it in 5 mins, amazing
@CleverSmart123
Жыл бұрын
Excellent explanation, that makes the notation much easier to understand. Thank you for this great video and sharing your knowledge
@AricLaBarr
9 ай бұрын
Glad it was helpful!
@vadimkorontsevich1066
2 жыл бұрын
That video had to be recorded... You and rikvitmath make the best econometrics videos on whole KZitem
@fotballfredrik1
6 ай бұрын
Very good and concise video!
@69nukeee
9 ай бұрын
Sweet explanation, loved it! Thank you very much!
@AricLaBarr
9 ай бұрын
You're very welcome!
@vickdeem
Жыл бұрын
Mind blowing! Practically made it easy to relearn the ARCH/GARCH framework. Thanks for sharing with us.
@AricLaBarr
Жыл бұрын
Glad it was helpful!
@ShihYangLin
2 ай бұрын
Thank you for this excellent video!
@stefankermer7782
9 ай бұрын
Hilarious explanation - thank you!
@cihangwang8044
Жыл бұрын
Oh wow, such a great video!
@mrivaden1972
Жыл бұрын
Excellent video!
@hectorpastor6981
Жыл бұрын
Great Video! Thank you for sharing
@AricLaBarr
Жыл бұрын
Thanks for watching!
@firstkaransingh
Жыл бұрын
Awesome explanation 👍
@asharablack
2 жыл бұрын
Thanks for these videos, I love the channel!
@ghada6763
Жыл бұрын
Never have I ever seen arch/garch models explained with this level of intuitiveness before. Thanks Prof. I have a few questions tho as this doesn't perfectly look similar to how it's written in textbooks. You explain that in the variance equation for ARCH1 the return variance at time t+1 depends on the lagged squared return (that's the variance) at time t, but isn't reserved for the GARCH model ? Because that's how almost every econometrics textbook explains it. And is the lagged squared "forecasted value" in the variable equation in GARCH as shown in the video, equivalent to the lagged squared error ? Again isn't that supposed to be seen in ARCH? I feel like things got mixed up for me.
@AricLaBarr
Жыл бұрын
So it really depends on terminology. A lot of econometric books will refer to the return at the error term because they are finding volatility for errors from another model as compared to the raw returns themselves!
@jbetanco7733
2 жыл бұрын
What do you do if the average of the returns are not zero? Excellent video by the way
@AricLaBarr
2 жыл бұрын
If the averages aren't 0 then you can model them! Maybe it is a basic model of just the overall average where you can just subtract that from your data before moving to GARCH modeling. But it could be more complicated! You could easily use an ARIMA model to forecast and model the mean then look at the residuals left from your model to use GARCH models on those!
@notasan
Жыл бұрын
Great video. thank you. You explain it well and in a non-boring manner. So, in the variance formula, the assumption is that it's the population variance with 1/t. for the sample variance with 1/(t-1) this assumption won't work.
@AricLaBarr
Жыл бұрын
Exactly! In the end, it really is just an approximation anyway. The goal is to try and get a good estimate of variance in the smallest time frame possible. That is why squared returns are a decent estimate of this value!
@notasan
Жыл бұрын
@@AricLaBarr thank you very much for taking the time to reply. it does make sense.
@svenunmuig7757
Жыл бұрын
Great video! Is it possible to teach something about the Barndorff-Nielsen and Shephard Model?
@AricLaBarr
Жыл бұрын
Thanks for your interest! Maybe in the future. For now, the next series is underway with anomaly detection.
@quangsonma2767
Жыл бұрын
Thank you very much, Profs, for this very handy video. I've been learning the Arch and Garch model and have really been struggling to deal with the notation and expression in the papers. Your way of addressing the problem is really straightforward and inspiring. Btw, could you pls help me to get a grasp of the residual terms in a GRACH model?. It's been making me confused for some time As we know, after estimating the parameters of a Garch model, for example, GARCH(1,1) model. So we can forecast the return of tomorrow's stock by the equation: r_(t+1) = σ _(t+1)* ε_(t+1) where σ _(t+1) is our forecast volatility for tomorrow from our GARCH (1,1) model and ε_(t+1) is i.i.d from N(0,1) distribution. That means the forecast return tomorrow is still unknown since ε_(t+1) is a random variable. So where we can get the fitted return for tomorrow and calculate the residual afterward?
@AricLaBarr
Жыл бұрын
More than happy to help! The goal isn't to get the return from these models, but just the volatility. You also have to be careful because we are assuming normality, but it is the actual variance of that normality that we are trying to model! For example, a lot of the times we assume that the returns are normally distributed around 0 but the ARCH/GARCH model is trying to model the variance of those normally distributed returns. Hope this helps!
@spp626
Жыл бұрын
Hello sir, should we have stationary data for applying GARCH model?
@AricLaBarr
Жыл бұрын
Yes! Now, a lot of times we do ARCH/GARCH models on residuals from other models so they should naturally be stationary (at least around the mean).
@harrishnandhan5689
Жыл бұрын
2:20 Sir how to create these charts?? Do we have to do this in E-views or Excel???
@AricLaBarr
Жыл бұрын
Those charts are created in Excel/PowerPoint. I find them really good at created professional charts in an easy way. Now, I don't use Excel for the analysis at all, but it is good for charting!
@vadimkorontsevich1066
2 жыл бұрын
the clue begins after 2:38
@efepeterman9352
8 ай бұрын
Hi I need a follow up on this. Point me in the right direction
@AricLaBarr
8 ай бұрын
Happy to help. Follow-up on the concepts or how to implement these?
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