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Carbon frontier

Hi all,

I am currently trying to build a carbon frontier - meaning, the tracking error of a portfolio regarding it's carbon intensity just like the picture :

so I have the intensities for each asset in "intensity"

But i think I have to link those two, knowing that the unknowns are w and TE :

intensity_portfolio =['w'].T, intensity)
TERP_1 = np.std(np.mean(wRP_1['w'].T * returns.sub(wEW['w'].T * returns, axis = 'index')) * frequency)

Or maybe I could find a way to build 30 portfolio or smth like this with 30 intensity target for the portfolio ( but i have to estimate the optimal weights that minimize tracking error for each portfolio.

Can you help me with that please?

Many thanks

1 response

If this is anything like the efficient frontier, it can be reduced to a quadratic problem and solved using CVXPY. A simpler approach could as well be to generate a number of random combination and select the smallest tracking error.