I see, so you will increase leverage to get the desired volatility, and then decrease leverage to get the desired returns?
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why would you decrease leverage to get the desired returns?
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The whole point of “volatility pumping” is that an asset with zero geometric return can be made to have positive geometric return if you reduce the leverage. My claim is that you can’t find assets with high enough volatility for that to be interesting in practice.
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That is a very simplistic view of what vol pumping is. Vol pumping isn't abut an asset with zero geo return, its just the most eye catching type. There are plenty of articles on vol pumping where cash isn't involved, and the "pumping" is just between a bunch of risky assets.
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This is exactly the same as saying that by diversifying you can reduce the volatility drag of a portfolio of assets, right? ie “diversification is the only free lunch in investing”.
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No it's not. The most diversified portfolio it not the right portfolio to rebalance back to capture the full effect. Typically the most diversified portfolio should be held and not rebalanced freqently to increase returns.
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Replying to @breakingthemark @macrocephalopod and
Yeah I agree. The "equivalent" thing would be the premium you tend to get from rebalancing assets with certain cross-sectional time-series properties - not the diversification (correlation) effect itself, I think.
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Replying to @therobotjames @breakingthemark and
Can you point me to somewhere that explains it in more detail? I feel like I have a pretty good mental model for these things, and (in a theoretical world with two assets with equal risk) it is obvious to me that frequent rebalancing to equal weight is optimal.
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Replying to @macrocephalopod @therobotjames and
The two assets need to have both equal risk and equal excess return (over risk-free rate) for optimal to be equal weight.
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Replying to @goldstein_aa @macrocephalopod and
The general formula for assets A and B is: (mu_A - r)/(sigma_A)^2 = (mu_B - r)/(sigma_B)^2. If that equation holds, then equal weight is optimal.
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Ah, yes I was assuming you don’t actually know the expected returns (ie return is proportional to risk). In practice you never have enough information for good estimates of mu.
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Replying to @macrocephalopod @goldstein_aa and
I simply plug the GMO estimates into my models
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