GAS model with Johnson SU distribution (Excel)

Generalised autoregressive score distributions shine the most when applied to flexible and generalised distribution families such as the Johnson SU function. Today we are investigating the implementation of the GAS model with Johnson SU distribution function in Excel.
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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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[@SlashnKashmir]

Only missing topic in this channel Principle Component Analysis

[@salardelavarqashqai]

thanks for sharing perfect ideas

[@sarthakverma7132]

Hello sir can you also do a video on the cook's distance (influence analysis).

[@nangbiaksing2622]

Hello Sir, i highly appreciate and benefits a lot from your videos, i request you to performed a test on generalized spectral analysis.

[@mottkey9122]

Hello, Savva. Thanks a lot for what you are doing. I have a question, are the newly created video lessons being added to your previously created playlists for Excel, Python, etc.?

[@avinashmishra6783]

Heyy Savva,
I need your help. I was recently hired by a merchant capital. I was more into dashboard but now my manager is asking me to go for portfolio optimization. I have gone through all your videos.
I do not have problem in concepts but rather in data. Some sub-indices are as new as 2017. If I take take data from 2017 for square matrix, I'm leaving historical data behind.
How do I form a square matrix when I the length of data-columns vary?

[@khaitran-de3ce]

Hi can u do videos about how to deal with irregularly sampled data 🤔

[@Daniel88santos]

This is really great content!!! Can you please do it in python (continuation of the Johnson SU python videos)? Also will be nice if you apply it to options or stocks buying example, like you did in the python series for the Johnson SU, I'm missing understand the application side of this models... Are we supposed to do Monte Carlo each time step forward with the updated distribution loc and scale values? In which way this models are related with GPR (Gaussian Processes Regression)? I mean conceptually. Can you explore GPRs implementation? Do you think it would be feasible to do something like Johnson SU Processes Regression? Or the GPRs already contemplates "deviations" from the normal distribution? Regards Daniel

[@basschalice]

i would love to see python implementatnion. It does not seem to be that complicated, so i'll try to do it myself

[@nitinmalik5614]

Do you provide any course quant course. if any please me email on which i cna contact you.