I got my Master in Financial Engineering. And in my opinion this content is of high quality and rare. Thank you for sharing such valuable knowledge on TH-cam.
Thank you so much for the detailed explanations. Been trying to connect the worlds of Stochastic Process & Computer Science for quite some time. Contributions like these help people like us break barriers which seemed almost impossible (until now). Thanks again and keep up the great work !! Respect !! Go Feynman !! ✌🏽👍🏽🙏🏽
I love your videos dude!!! You put in so much work and effort in them! thank you for this. Can you suggest any readings for deciding a stocks weight in a portfolio(maybe include this volatility aspect too). Thanks!
Excellent Tutorial! Had two conceptual questions: 1) Do you happen to know why the OU process the natural choice to incorporate volatility clustering (what is the connection between mean reversion in OU and the volatility autocorrelation found by Mandelbrot)? Would a simple AR(1) process for volatility work too? 2) Do I understand it right that OU addresses only the volatility clustering property, but not the heavy tails and excess volatility pptys of stock returns? Thanks for any feedback!
Thanks for this video , in the past you created a video on statistical properties of the bars by the work of Marcoz Lopez De Prado (Advances In Financial Machine Learning) could you perhaps create a few more videos showing implementation of something like meta-labelling etc...
Very cool video and project. I tried to replicate the notebook myself from your site. I am working on a project where I want to find SV under P so its what I am looking for. But shouldn't you use Cox-Ross Model for Volatility? I would like to see a follow-up video on this topic! On calculating these parameters on historical data
Can I ask a serious question? How often are you actually correct with this model? Meaning, how close does the model actually come to accurately measuring realized volatility?
Re #2:50: assuming that by `delta S_t` is meant `S_t+delta - S_t`, the given formula doesn't make sense to me: From `X_t = ln(S_t)` follows `r_t = ln(S_t+delta) - ln(S_t) = ln(S_t + (S_t+delta - S_t))` which cannot be right(??) E.g. With prices S_t=20 and S_t+delta=21, this would entail: `r_t = ln(21) - ln(20) = ln(20 + (21-20)) = ln(21)` 🤨🤨🤨 And then the code expresses something different entirely: `r_t = ln(S_t+delta / S_t)` which actually makes sense to me. What's up with that `ln(S_t + delta S_t)` in the formula though?!?
This is super quant stuff. I'm a trader. Look, can you just put a flashing pop up on my screen that says "Buckle up, we're about to get volatile" Can a quant do that? They must be able to if a human brain can do it, right?
This is very good content! I am in the process of migrating from Excel to Python. How do you plot the text and algebra notation (at around 5 min. for instance) in your notebooks?
can you explain too me, please, why would you use OU proces to model variance, im not an expert, i just like math and coputer science, i'm been using, garch model to model and forecast variance
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I got my Master in Financial Engineering. And in my opinion this content is of high quality and rare. Thank you for sharing such valuable knowledge on TH-cam.
my PhD is in stochastic processes, and yes, he does good work.
Thank you so much for the detailed explanations. Been trying to connect the worlds of Stochastic Process & Computer Science for quite some time. Contributions like these help people like us break barriers which seemed almost impossible (until now). Thanks again and keep up the great work !! Respect !! Go Feynman !! ✌🏽👍🏽🙏🏽
All respect and appreciation!!!
Although I'm not a quant, I watch your videos for fun. I'm a math student right now, but I hope to become a quant one day.
Pure gold. My respect.
This channel is precious. Thank you very much
Great explanation! Do you have any further textbook recommendations where these concepts are presented?
I love your videos dude!!! You put in so much work and effort in them! thank you for this. Can you suggest any readings for deciding a stocks weight in a portfolio(maybe include this volatility aspect too). Thanks!
I implement the RFSV model last year, it also uses a similar OU process in there
Excellent Tutorial! Had two conceptual questions:
1) Do you happen to know why the OU process the natural choice to incorporate volatility clustering (what is the connection between mean reversion in OU and the volatility autocorrelation found by Mandelbrot)? Would a simple AR(1) process for volatility work too?
2) Do I understand it right that OU addresses only the volatility clustering property, but not the heavy tails and excess volatility pptys of stock returns?
Thanks for any feedback!
Thanks for this video , in the past you created a video on statistical properties of the bars by the work of Marcoz Lopez De Prado (Advances In Financial Machine Learning) could you perhaps create a few more videos showing implementation of something like meta-labelling etc...
really outstanding content. keep it up!
This video is simply amazing
Very cool video and project. I tried to replicate the notebook myself from your site. I am working on a project where I want to find SV under P so its what I am looking for. But shouldn't you use Cox-Ross Model for Volatility? I would like to see a follow-up video on this topic! On calculating these parameters on historical data
Hi, why you pick rolling days 40 for vol? why not calc vol from beginning? Thank you for advising!
Can I ask a serious question? How often are you actually correct with this model? Meaning, how close does the model actually come to accurately measuring realized volatility?
Thanks for the video!
Really good - thank you!
How could you simulate historical implied volatility?
that little bit of correlation is there because outliers
Re #2:50: assuming that by `delta S_t` is meant `S_t+delta - S_t`, the given formula doesn't make sense to me:
From `X_t = ln(S_t)` follows `r_t = ln(S_t+delta) - ln(S_t) = ln(S_t + (S_t+delta - S_t))` which cannot be right(??)
E.g. With prices S_t=20 and S_t+delta=21, this would entail: `r_t = ln(21) - ln(20) = ln(20 + (21-20)) = ln(21)` 🤨🤨🤨
And then the code expresses something different entirely: `r_t = ln(S_t+delta / S_t)` which actually makes sense to me. What's up with that `ln(S_t + delta S_t)` in the formula though?!?
If kappa is significantly different from zero then your daily log returns should be correlated.
İt's awesome lecture. Thanks a lot dude 👍
Cheers, feel free to suggest ideas for future videos anytime
Can potentially use scoring algorithm to estimate MLE parameters. But great video.
Great suggestion, that would be an improvement. Next time I’ll use the fisher scoring algorithm if I’m doing MLE 👍
This is super quant stuff. I'm a trader. Look, can you just put a flashing pop up on my screen that says "Buckle up, we're about to get volatile" Can a quant do that? They must be able to if a human brain can do it, right?
This is very good content! I am in the process of migrating from Excel to Python. How do you plot the text and algebra notation (at around 5 min. for instance) in your notebooks?
He is using LaTEX typed into a markdown cell within Jupyter Notebook. Hope that helps!
Awesome Content dude! Love this channel a lot. Could you consider to make a video related to term structure models?
Definitely will get to these videos
can you explain too me, please, why would you use OU proces to model variance, im not an expert, i just like math and coputer science, i'm been using, garch model to model and forecast variance
OU process is a mean-reverting process commonly used to model processes pulled back towards a central value (like interest rates or a dampened spring)
Why do we square the log returns instead of getting the absolute value?
I had this same question. I think square of log returns refers to variance of log returns (ie it is a measure of volatility).
This is literally rocket science
Dude, you pronounce all names totally wrong.
Thanks, I also struggle in pronouncing peoples names as well - I prefer numbers