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Defining asset classes from a quantitative perspective is an interesting question that is not really addressed "officially" as far as I know. Let's try to write some requirements you want strategic decisions to make sense: each asset class should have at least one or two different "economic drivers" than the others you want tactical ...


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(I take it that 5 out of 10 assets is just an example, because in this case all combinations could easily be checked.) Here would be an example how to do it in R with an algorithm called Threshold Accepting. library("neighbours") ## https://github.com/enricoschumann/neighbours library("NMOF") ## https://github.com/enricoschumann/...


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As @stans already said in the comments to your question, the existence of the market portfolio hinges on the existence of a risk free rate $r_f$, where risk free, in this context, means that its value can be perfectly contracted for the relevant return horizon, e.g. you will with probability one get that rate for 1 month or 1 year. In theory, we must also be ...


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Examples of statistical measures to compare extreme rebalancing: Below, I have provided some examples of statistical measures, that compare the extreme re-balancing of different portfolios. They do not show how the concentration is allocated, only if the allocation is extreme. Many of the measures can be found in the empirical portfolio section of Patton et ...


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This is indeed a convex, quadratic, integer programming problem. Most of those are NP-hard, so don't beat your brains to find the optimum efficiently. That said, nowadays these problems can be solved efficiently, approximately but with high accuracy with convex solvers (e.g. Mosek). From my own experience, this could work up to sizes of thousands of assets, ...


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I worked on volatility control funds for a few years a while back. Frequently, we would simply use the average as the threshold between high and low vol. That is because volatility is below average in normal times, but can quickly exceed the average during a correction. Mathematically, the fat right tail of an inverse-gamma distribution for an unknown ...


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Letting the Markowitz portfolio over period $i$ be $\hat{w}_i = \hat{\Sigma}_i^{-1} \hat{\mu}_i$, the distributions of these are asymptotically normal around the population value: $$ \hat{w}_i \sim \mathcal{N}\left(w_i, \Omega_i\right), $$ where $w_i$ is the population value and $\Omega_i$ is the covariance of the sample Markowitz portfolio. If the ...


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There are two primary asset allocation decisions: Strategic asset allocation: This tends to be a long-term, passive portfolio mix that an institution would hold if there are no active views. In principle, measuring success/failure is easy – does the SAA process generate the necessary return profile to support an institution's needs/missions? For example, ...


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A quant technique that could be used to (partially) address this problem is the Mean Variance Spanning Test of Huberman and Kandel (1987). Abstract This is a statistical test of whether adding K new assets to an existing set of N assets improves the Efficient Frontier or not. Roughly speaking the test involves checking whether the new assets "add ...


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In fund accounting, it each hedged position you open as an asset & liability that has to be netted off. Management and Performance Fees are incured will be based on the AUM after netting off all the assets and liabilities of the positions.


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From a managed fund operation perspective, the 2% charge is on how much funds one investor controls not the total FUM of the fund.


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Not necessarily an answer but was too log for a comment. I guess a starting point will be the papers outlined here. That said, if you think of it intuitively, it's not surprising that the choice of your assets matters most. I am a big sceptic when it comes to skills of asset allocators so I am not surprised if that number is 90% or higher. Especially since ...


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So let me begin with why both of these models concern me. In the first model, the Frequentist Markowitzian model, Ito's method assumes that all parameters are known. No estimators are needed. That is a big deal because White in 1958 proved that models with $\tilde{w}=Rw+\varepsilon, R>1$ have no solution if $R$ has to be estimated in the Frequentist ...


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I think the optimization problem you quote was clumsily assembled, but there is a sensible idea here. I can explain a variant, based on minimax Frequentist 'risk'. (The 'risk' here is risk of making a bad decision, not portfolio risk.) Suppose you will observe a matrix of historical returns, $X$ then create a portfolio based on that data, call it $w(X)$. The ...


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