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Out-of-sample is basically impossible to predict means. Second moments are much easier. You can take a look at this post: Estimating $\mu$ - only increasing $T$ improves estimate? Only with infinite $T$ you would be able to correctly estimate $\mu$. So theoretically your procedure could be correct if means are time-varying, but out of sample I bet your ...


One way to this is the following (you can code all these constraints if you use the right software, I am doing such things using mathematica) You define $w_{i,j}$ which is the weight of asset $j$ in subportfolio $i$, furthermore you define $w =(w_j)_{j=1}^{\text{no of assets}}$ the total weight of the portfolio in asset $j$. the objects for the ...


Possibly she is referring to the fact that classical PCA is not robust in the sense that its asymptotic properties depend on the distribution of the data. Large deviations from normality will result in sub-optimal estimates, or estimates that are distorted. If this is what she has in mind, then you can use robust PCA instead (cf. Candes et. al.)


You can avoid cancel/replace using pegged orders. Depending on your model that could be very useful.


You're writing it in terms of the growth factors and annual compounding. You want to split up $M$ so that as each piece grows over time, the $i$th person at time $n_i$ gets paid the same amount as the $j$th person gets at time $n_j$. So simply scale by the corresponding discount factors. Let $$ \alpha_i = \frac{(1+r)^{-n_i}}{\sum_j (1+r)^{-n_j}} $$ Then ...


First it would help to know some more details about what you mean by maximum capacity. However here are a few things to consider. Do you have a simulator you use to simulate your strategy with market data? If answer to above is yes then you can clearly see the linear impact due to market liquidity constraints for your increased size. Now 2 is easy only if ...

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