I am at lecture 11 right now (volatility modeling). I guess the best thing I learned was value at risk models and how companies like Morgan Stanley use historical returns, covariance matrices, and monte carlo to estimate their maximum risk. That was fascinating.
Other than that, I dived here and there and found the portfolio optimization lectures to be good. To be honest, all lecture taught by people from the industry are damn good.
As for the strategies, eigen portfolio would be one. I started reading about it and digressed to another blog (https://srome.github.io) that had some excellent resources. From there, I had to read a few more papers to get to MSR. That makes three strategies -> Eigen portfolios, Minimum Variance, and Maximum Sharpe.
The last strategy is using a genetic algo to maximize sharpe ratio. That is a custom implementation as I've quite some experience with GAs.
Happy to talk more about the course and the strategies.
Comments
Can you please give us a TLDR of MIT's 18.S096 and the strategies from the course you tried to adapt into this package?
Oh sure.
I am at lecture 11 right now (volatility modeling). I guess the best thing I learned was value at risk models and how companies like Morgan Stanley use historical returns, covariance matrices, and monte carlo to estimate their maximum risk. That was fascinating.
Other than that, I dived here and there and found the portfolio optimization lectures to be good. To be honest, all lecture taught by people from the industry are damn good.
As for the strategies, eigen portfolio would be one. I started reading about it and digressed to another blog (https://srome.github.io) that had some excellent resources. From there, I had to read a few more papers to get to MSR. That makes three strategies -> Eigen portfolios, Minimum Variance, and Maximum Sharpe.
The last strategy is using a genetic algo to maximize sharpe ratio. That is a custom implementation as I've quite some experience with GAs.
Happy to talk more about the course and the strategies.