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An approach to consider is: Computing the total return streams of all the instruments in the portfolio Calculate the risk parameters using 1 Weight appropriately (Equal risk contribution, min variance etc)


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That's the way you apply. Usually you get the closest number of shares possible. However, if you use that strategy you are very likely to underperform the market. Check table 3 on this paper for the Out of sample performance of the Markowitz strategy. Over their sample the Sharpe Ratio is 0.07 whereas a simple naive strategy 1/N yielded 0.18.


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If you're in Excel, get the returns of both portfolios into 2 columns, matched up by time. The "correl()" function will get you the correlation coefficient.


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You will need the covariance matrix to calculate this. Say you have a collection of $n$ assets. The value of asset $i$ is represented by the random variable $X_i$ and the corresponding portfolio weight is are $w_i$, and $v_i$ for the two portfolios. The correlation between the two portfolios is: $$ \frac{\sigma(w^TX,v^TX)}{\sqrt{(w^T\Sigma w)(v^T\Sigma ...


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One standard approach is to shrink your forecasts towards zero (or to some reasonable value as in the Black-Littermann model). Shrinking towards zero is done by: $$w^*=\underset{w}{\text{argmax}} \ \ \lambda_{\alpha} r^Tw - \lambda_r w^{T} \Sigma w - tradingCost(|w-w_0|)\\$$ $$0\leq\lambda_{\alpha}\leq1$$ Shrinkage coefficient $\lambda_{\alpha}$ is best ...


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FVIX is not hard to compute. Just regress changes in VIX on excess returs of your base assets (it can be the 25 FF portfolios if those are what you are trying to explain) i.e run the following: \Delta VIX_t = X_t\beta+\epsilon



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