Yeah and this is a much more intuitive way of generalising from the n = 2 case. Weights are proportional to inverse variance even for n > 2. Importantly this assumes independence so it doesn’t translate to portfolio optimisation very easily.
Your slippery slope makes no sense to me. What do we need XML for here? Is anybody asking for it? You can use your own grammar checker but you can't render your own equations and submit them.
Even if you personally had a mathjax extension, you would still be prevented from explaining math to others, unless you could convince everyone to install it.
ADDED. Because the new functionality will be used to create cutesy effects for reasons that have nothing to do with communicating math, increasing the demand for moderation work.
Why? Latex is not how maths if supposed to be read, else we'd all be doing that. It's how it might be written.
edit: Nobody is going to use maths for cutesy effects. Where have you ever seen that happen? Downvote them if they do. It is not going to be a big deal.
Let w be the vector of weights and S be the comformable matrix of covariances. The portfolio variance is given by w’Sw. So just minimize that with whatever constraints you want. If you just asssume weights sum to one, it is a classic quadratic optimization with linear equality constraints. Well known solutions.
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Write v_i = Var[X_i]. John writes
But if you multiply top and bottom by (1 / \prod_{m=1}^n v_m), you just get No need to compute elementary symmetric polynomials.If you plug those optimal (t_i) back into the variance, you get
where `H = n / (\sum_{k=1}^n 1/v_k)` is the Harmonic Mean of the variances.