You can find the spreadsheets for this video and some additional materials here: drive.google.com/drive/folders/1sP40IW0p0w5IETCgo464uhDFfdyR6rh7 Please consider supporting NEDL on Patreon: www.patreon.com/NEDLeducation
Hi - Thank you very much for these excellent videos. This one was excellent, and between this presentation, and those on Sharpe Ratio and Treynor Ration - they almost touches on the Omega Measure. I would be interested in learning to use the Omega Ratio to optimize allocation for a risk level, and comparing the allocation arrived at by maximizing the Sharpe Ratio. Thanks again.
@@NEDLeducation Optimizing Sharpe ratio (even with geometric standard deviation) or Omega ratio are very interesting but seems not to be efficient to reach out the Holy Grail of portfolio as both result in portfolios with lack of diversification which underperform Most Diversified Portfolio (MDP). Am I close to be right ? Would it be more meaningful to use Yang-Zhang volatility (if possible, great NEDL’s tutorial about it : th-cam.com/video/1m4cffFj3PE/w-d-xo.html) to calculate/optimize Sharpe ratio or Omega ratio ?
Brilliant video, thanks! One question, how would you do an optimization also including historical average returns on the different investment categories? So that both return and diversification was optimized.
Hi Rickard, and glad you enjoyed the video! Excellent question, the diversification ratio optimisation is something that takes into account the covariance matrix only. However, you can in principle build an "efficient frontier" in return and diversification ratio, maximising the diversification ratio while requiring some minimum acceptable average return as a constraint.
Hi, and glad you liked the video! As for your question, it is a pretty broad one, but a very good short introductory textbook for quantitative finance is "151 trading strategies" by Kakushadze and Serur. For a more fundamental and classical textbook format, few can beat "Investments" by Bodie, Kane, and Marcus.
Hi Savva , there is a closed form solution for the Maximum Diversification Portfolio , by Pemberton Rau (2007). But I can't understand why in the Lagrange function there is the constraint that w'σ=1. Can you explain why?
Hi, and thanks for the comment! The formula you present is probably due to normalisation however I will need to double-check this in the original source.
This was really great, thanks I am currently implementing into python but the logic in excel makes this simply to do. Quick question, why was the assumption that the hypothetical portfolio variance was derived from perfectly correlated assets? Would that not increase risk per the MPT?
Hi, and glad you liked the video and are implementing the model in your own Python code! Thanks for the question, the hypothetical variance is there for comparison only - we measure the ratio between volatility in absence of diversification (when all assets are perfectly correlated) and the real-world volatility calculated using the covariance matrix. This maximises the diversification benefits an optimised portfolio provides. Hope this helps!
Hi, and glad you liked the video! The minimum variance portfolio is a different concept to the diversification ratio, MVP would prioritise investing in low-volatility assets while diversification ratio would optimise the correlation structure between assets. I have got a series of videos on efficient portfolio frontiers where I consider MVP, check it out if you are interested: th-cam.com/video/fjEkkVwRl2A/w-d-xo.html Hope it helps!
I calculated the GMVP. It has r = 6,08% and std = 6,52% (SR = 93,17%) The MAX_DR portfolio you calculated is not on efficient frontier, as it has r = 8,92% and std = 8,28% While the mean-variance efficient portfolio of the same risk has r = 9,58% and std = 8,28% So by optimizing diversification ratio in this definition we get Sharpe ratio decrease from 115,73% in mean-variance case to 107,82% in maximizing diversification ratio case (which is this)
Sir I'm making a Research Proposal on Optimum Portfolio using SIM and Markowitz Model. Can you please help me in knowing the areas which I can study further... It'll be a great help to me.
Hi Sam, and glad you liked the video! I have got several videos on portfolio management, including Markowitz theory. Check the portfolio management playlist: th-cam.com/play/PLE4a3phdCOatsDWPgNzzAfLz3eLwNwLnv.html Hope it helps!
You can find the spreadsheets for this video and some additional materials here: drive.google.com/drive/folders/1sP40IW0p0w5IETCgo464uhDFfdyR6rh7
Please consider supporting NEDL on Patreon: www.patreon.com/NEDLeducation
Another excellent video, clear and no-nonsense. Many thanks!
Excellent video, such a clear explanation. Thank you.
Very informative video Sir
Love from India❤️
Hi, Thanks Bro for you kinds and you're have spread science and knowledge to the people
Learning so much from these great videos! Would it be possible to do run this same optimisation but with broker leverage?
Hi - Thank you very much for these excellent videos. This one was excellent, and between this presentation, and those on Sharpe Ratio and Treynor Ration - they almost touches on the Omega Measure. I would be interested in learning to use the Omega Ratio to optimize allocation for a risk level, and comparing the allocation arrived at by maximizing the Sharpe Ratio. Thanks again.
Hi, and glad you are enjoying the videos! I would definitely do a video on the Omega ratio at some point in the future!
@@NEDLeducation Optimizing Sharpe ratio (even with geometric standard deviation) or Omega ratio are very interesting but seems not to be efficient to reach out the Holy Grail of portfolio as both result in portfolios with lack of diversification which underperform Most Diversified Portfolio (MDP).
Am I close to be right ?
Would it be more meaningful to use Yang-Zhang volatility (if possible, great NEDL’s tutorial about it : th-cam.com/video/1m4cffFj3PE/w-d-xo.html) to calculate/optimize Sharpe ratio or Omega ratio ?
awesome! If I use weekly day should I multiply by 52 and monthly data by 12? TIA
Hi Ricardo, and glad you liked the video! Yes, indeed.
Thank you very much!
Brilliant video, thanks! One question, how would you do an optimization also including historical average returns on the different investment categories?
So that both return and diversification was optimized.
Hi Rickard, and glad you enjoyed the video! Excellent question, the diversification ratio optimisation is something that takes into account the covariance matrix only. However, you can in principle build an "efficient frontier" in return and diversification ratio, maximising the diversification ratio while requiring some minimum acceptable average return as a constraint.
@@NEDLeducationhi, thanks for your videos! Is there a video of the creating the efficient frontier on your channel?
could u share the solution to optimize weights without using solver, thanks
Very useful . Thanks. Have you got any books you would recommend for financial modelling?
Hi, and glad you liked the video! As for your question, it is a pretty broad one, but a very good short introductory textbook for quantitative finance is "151 trading strategies" by Kakushadze and Serur. For a more fundamental and classical textbook format, few can beat "Investments" by Bodie, Kane, and Marcus.
Should I use python libraries or should I use scipy optimize myself? What would be better?
Hi Savva , there is a closed form solution for the Maximum Diversification Portfolio , by Pemberton Rau (2007). But I can't understand why in the Lagrange function there is the constraint that w'σ=1. Can you explain why?
Hi, and thanks for the comment! The formula you present is probably due to normalisation however I will need to double-check this in the original source.
This was really great, thanks I am currently implementing into python but the logic in excel makes this simply to do. Quick question, why was the assumption that the hypothetical portfolio variance was derived from perfectly correlated assets? Would that not increase risk per the MPT?
Hi, and glad you liked the video and are implementing the model in your own Python code! Thanks for the question, the hypothetical variance is there for comparison only - we measure the ratio between volatility in absence of diversification (when all assets are perfectly correlated) and the real-world volatility calculated using the covariance matrix. This maximises the diversification benefits an optimised portfolio provides. Hope this helps!
Hello! Nice video! Did you get the global minimum variance portfolio (GMVP) as a result?
Hi, and glad you liked the video! The minimum variance portfolio is a different concept to the diversification ratio, MVP would prioritise investing in low-volatility assets while diversification ratio would optimise the correlation structure between assets. I have got a series of videos on efficient portfolio frontiers where I consider MVP, check it out if you are interested: th-cam.com/video/fjEkkVwRl2A/w-d-xo.html Hope it helps!
I calculated the GMVP. It has r = 6,08% and std = 6,52% (SR = 93,17%)
The MAX_DR portfolio you calculated is not on efficient frontier, as it has r = 8,92% and std = 8,28%
While the mean-variance efficient portfolio of the same risk has r = 9,58% and std = 8,28%
So by optimizing diversification ratio in this definition we get Sharpe ratio decrease from 115,73%
in mean-variance case to 107,82% in maximizing diversification ratio case (which is this)
@@Kuznetsovication That does look correct!
Sir I'm making a Research Proposal on Optimum Portfolio using SIM and Markowitz Model.
Can you please help me in knowing the areas which I can study further...
It'll be a great help to me.
Hi Sam, and glad you liked the video! I have got several videos on portfolio management, including Markowitz theory. Check the portfolio management playlist: th-cam.com/play/PLE4a3phdCOatsDWPgNzzAfLz3eLwNwLnv.html Hope it helps!