Hey Daniel. Yes, the fame and money got a bit too much. Could barely leave the house without some groupies wanting me to sign some part of their body, and these guys are HAIRY. I hope to be making more content soon. Thanks for reaching out. Tino

Nevermind, just realized what transition density you were using!

Hi mate, marginals is just a fancy word for the distribution of the 2 separate datasets. So time can have a certain distribution and money spent a totally different one. These 2 datasets are the marginals.

You are super welcome

Welcome mate!

True annoying background noise. I closed the video because of this

Lol. Sorry to disappoint.

@@dirtyquant disappointment just like my son

Hi mate. Can you post this on dirtyquant.com and I will look at it later

Glad you liked the video. Does the exogenous variable have a correlation with GARCH characteristics? You can’t just throw anything in there as it won’t converge. Just add it as another asset and see what it looks like. No problem in doing that. Remember we are looking at correlation of VOLATILITY, not returns.

@@dirtyquant Thanks for replying X has garch characteristics i want to check whether variable X impact the dynamic conditional correlation of two assets..like if it has positive or negative effect

It won’t have any effect on the other 2 variables. But you can look at the time series of the DCC and I find that useful. Let me know how you get on. You can post your findings on dirtyquant.com

Hi mate. DCC automatically does 1 step ahead forecast

@@dirtyquant Hi, I'm quite curious how to modify the code if I want 30 days ahead forecast. Do I just change the looping through Rt?

Link in the description to my GitHub with all the code

Hi, yes, but that will give you the correlation of volatility, not of the returns, as it’s way more stable.

@@dirtyquant Hi Dirtyquant, Thanks so much for getting beck so quickly! Is there any way that I can modify the code to get the return correlation. I tried input return instead of udata_list but the model shows not success.

The model is (likely) not going to converge if you use returns as they as far too unstable. You need to convert the returns to a uniform distribution using the appropriate CDF for that distribution. Again, I don’t think it will work as the autocorrelation of returns decays far too quickly so it will be incredibly noisy. Please try it and report back. :-)

Thank you! I’ll try it out 🥹

Welcome!

Thank you. That is very flattering. I hope to make many more

Glad you like it mate!

Ha! That’s a million dollar question. I had been working for 7 years before I did my PhD. Certainly provided a different perspective. There is no right or wrong way. The advantage of going straight after undergrad is that you have a PhD earlier, disadvantage is that you STILL have no experience and no business network once you are done. There is also a certain maturity that would benefit something as long and frustrating as a PhD. I would say, depends on your personal situation, but I would prefer going after some work experience (if you can detach yourself from the salary teat)

@@dirtyquant thanks for your perspective. I asked this as an undergrad, who is sitting here, one week from graduation from my bachelors degree, with two offers, one for a funded MS in statistics and a fully funded PhD in statistics. My last two years of school I really enjoyed classes and learning stats, and thought I would want to do a phd immediately. However, after I started reading more and more about the applications of statistics to finance, and especially the roles in quantitative research and analysis that are out there in hedge funds, investment banks and other financial institutions, it’s like that passion and interest for doing it has been erased for now. I have opted for going to my MS program and to try and land such a role to work first, and then possibly go back, because I have not found the drive to really go straight out of undergrad. It sucks cause I thought it was really passionate about this whole academic journey and now going back on what I originally planned on feels like a big deal. I guess what your saying is it must vary from person to person. For me I feel like after just getting a break from school after my MS and working will just clear my mind a bit before a phd. I also just felt very uncertain about going into a phd immediately, due to my lack of actual industry experience. However, over the last week, this has been a constant back and forth, and still I just don’t even know if me denying the phd offer is me making a huge mistake. I just figured I should listen to my gut and if I feel like I need a break I need a break. What do you think about this?

Hey Julian, I am out travelling (at PyCon actually) so can’t look into this too much, but you could just re-mean it to whatever you like. You subtract the average mean, and punch in the value that you like. Easy dirty quant solution!

@@dirtyquant Appreciate the answer! I could do this, but I want to use the simulated price paths in an academic paper so I need to justify decisions like this.. you see what I mean? Enjoy PyCon!!!

@@juliangoogle6022 now you got me thinking. Let me try and run some stuff now and see what is up. When you get an ear worm like this you can’t let it slide. Will get back to you

Ok, so I ran the simulator and it actually looks fine. There is a distribution for the mean as well. Sometimes it will be more, sometimes it will be less. It won't be a constant for each of those runs. Run the notebook and then look at sim_df.mean(axis=0).mean() It gave me something that's within the confidence interval of the output of garch_output.summary at the beginning of the notebook It will hold for academic papers 🙂

@@dirtyquant Yes so the mean of the sim_df.mean is consistent with the mu value of the GARCH model, but I suspect the mu value (and with that also the confidence intervals in the garch_output.summary) to be too optimistic, no? If I take the mean of the returns you pull from yahoo I get 0.021428 (with the command returns.mean()). And the mu in the GARCH model is 0.0524 --> more than double? So as a result all the price paths simulated with the GARCH model will be much more bullish than the real data we pulled right? For me, this doesn't make sense logically: I throw data with mean 0.02 into a model and the model returns the mean 0.05. This also makes the forecasts unsuitable for backtesting because they are way too bullish

Thank you David. Glad they help

If I compare it to the price paths I simulated with the Geometric Brownian motion, they pretty much stick to the drift and the random walk component, also in the long run ... How come the GARCH model loses its stated "constant mean" after a while?? And is there something I can do to avoid it? My idea is to use the log returns instead of the normal daily returns to "normalize" and hopefully stabilize the model a bit. EDIT: Using log returns didn't change a lot, I obtain very similiar results

Thanks!

​@@dirtyquant Hey you are even answering to comments, amazing! :D Quick question: The simulated price paths all have certain characteristics from the "real" returns in common, right? I am backtesting a strategy and would like to see how much those characteristics (I assume it's the mean and the standard deviation) can change until my strategy doesn't work anymore. Does this make sense to you? Where do I actually see what "characteristics" the GARCH simulator pulls from "returns"? How could I insert values here manually to see when my strategy doesn't work anymore? Thanks already!

@@juliangoogle6022 Hey, so you can play around with the Beta (the memory) and the alpha (the shock) which will define how much "Garchness" there is. At the same time, mu (the mean) nu (the degrees of freedom) and lambda (the skewness) will also define what the return series look like. I would start by fixing all the parameters except one, and changing it whilst feeding into your backtester and see when things start breaking down. most of these won't have massive realistic ranges (Beta is always around 0.9) so your search universe won't be huge. Hope that helped.

@@dirtyquant Okay got it, thank you! I think for the analysis, it would be more intuitive to a reader to see how much the volatility or the mean return can change until the strategy breaks instead of the greek parameters, is there a way to translate that in this case?

I also simulated price paths with the Geometric Brownian motion, here it is easy because the whole model consists of the mean and the volatility but I would love to do it with GARCH because this takes the volatiltiy clustering into account

Thank you!

Thank you. I am flattered

Ha! Glad it helped you out. I always struggled too but one day it clicked. Now I want to tell people about it :-)

256 days in a year. Square root is 16. Multiplied by 100 to make 0.01 or 1% equal to 1.00 in the code. 16 x 100 = 1600 Thanks for checking!

Glad it helped

Haha. Sorry I can’t please everyone. Merry Xmas :-)

Appreciate the feedback mate! Thank you!

Very happy to hear you found some value Luke