# Tag Info

4

The Lyxor white paper Regularization of Portfolio Allocation contains a lot on this topic. The head of quant research there, Thierry Roncalli, also held a talk about this recently.

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It depends on what you want to optimize with transaction costs: liquidation hedging allocation The two best reference I have in mind are: Gökay, S., Roch, A., Soner, 2011. Liquidity models in continuous and discrete time. In: Di Nunno, G., Øksendal, B. (Eds.), Advanced Mathematical Methods for Finance. Springer Berlin Heidelberg, pp. 333-365. URL http://...

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From a general point of view and to answer directly to your originial question, you should only have to modify the inputs to the MATLAB function you refer to. As a matter of fact, fmincon is an optimizer looking to process a broad variety of problems as explained in the documentation: fmincon attempts to find a constrained minimum of a scalar function of ...

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In robust optimization, the true return is not known, we just have a prior $\alpha$ and you have to take into account a possible misestimate which can lower the true return. This is done under the assumption that the posterior return will be within the prior return $\alpha$ plus minus the error being in some $\sigma$-interval. Now a try for a more formal ...

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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.)

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Not exactly the answer you're looking for: It's not obvious that a region of stability is a desirable property. One can trivially construct an example where this is true: suppose the actual generation function of your target is $f: x_t \mapsto 2x_t+1$ over $\mathbb{R}$, and you have a signal $s$ of one parameter $s\left(p,x_t\right)=\left(p^{e} \mod 3\... 1$R^{2}$is a measure of goodness of fit. You can calculate it regardless of the type of linear regression model. However, it may not always have value. For instance, if you have an extreme outlier in your data, then a classic$R^{2}\$ will typically be lower than you expect (because there is variation in the data that your model is not picking up). ...

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Thanks for the answers and comments above. In particular to Eric Brady, who had me reading a lot of Bayesian papers. In the end, I think the answer to the question is that on the monthly time-frame robust factor algorithms aren't really necessary. On daily and lower time frames, large spikes in returns due to events (earnings ect.) can really mess with ...

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Whether or not it is flawed in practice depends on dynamic the risk exposures really are. Many factors or indices used for style analysis actually require dynamic trading to maintain - so you could potentially have a fund that trades a lot while still generating a return series that can be be modeled out of sample with static exposures. One relatively ...

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