I am trying to refresh my knowledge of portfolio risk calculation but would like to get a second opinion on the best approach.

I have a set of 10 assets that together make up the benchmark and I have the benchmark weights available. I also have a lot of historical data so I would be able to estimate a predicted covariance matrix on that basis.

Within these 10 assets I have a view on 4, 2 asset I would like to overweight, 2 asset I would like to underweight. Not all assets have the same contribution to portfolio risk, so I would want to do some scaling to keep many active calls balanced.

The simple approach would be to scale the position by the contribution to portfolio risk, such that my underweights and overweight have the same contribution?

I am also looking at a more sophisticated approach where I control the overall level of active risk ex ante. Is there a straightforward way to rebalance these two assets with, say, a 2% active risk budget without running a full optimisation?

Also any ideas on how to avoid broader rescaling / rebalancing of the other 6 assets I want to keep mostly untouched would be appreciated

I am looking for an implementation in python




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