Hot answers tagged modern-portfolio-theory
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Bernd Scherer has done exactly this test in his text "Portfolio Construction and Risk Budgeting 4th Edition". There is an SSRN paper by Scherer called "Resampled Efficiency and Portfolio Choice (2004)" you can take a look at as well.
I would suggest you skip re-sampling (especially if you have a long-only portfolio) and take a look at Meucci's Robot ...
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This pdf says on page two that the paper was never published. I don't know the reason but you could try to mail the authors of the papers were the article is mentioned. Since it was never published it might be less encumbered by copyright than usual.
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Strictly speaking the risk aversion coefficient depends on the form of investor preferences. Your "multi-objective evolutionary algorithm" may or may not be easy to place in this format. However, it becomes easy if you think about the risk aversion coefficient in mean/variance space if you were a mean-variance variance investor.
In this case you would have ...
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Any explanations? Yes. Within each asset category we find that stocks may be:
Unattractively underperforming the category norm
Attractive as they meet the expected norm
Unsustainable as their returns exceed the category norm and may suffer mean reversion
By focusing on low variance, we exclude type (3) stocks that damage portfolio performance through ...
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Such an article, if written in English, would get laughed at so hard by the blogosphere the authors would be shamed into doing a bit more research on Wikipedia next time before claiming a fully automated AI system is "the first of its kind."
This guy's main competitive advantage is in pitching to non-English speaking Danes who don't have a clue what's ...
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See "Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size" by Ledoit and Wolf.
http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aos/1031689018
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There is a simple reason to use prefer $CE$ to pure utility: $CE$ is independent of utility units. Thus it allows direct comparison.
The cash equivalent of a risky portfolio is the certain amount of cash that provides the same utility that portfolio. So for portfolio $w$ we can define $CE$ via $U(CE)=E[U(w)]$ or $CE=U^{-1}(E[U(w)])$. Note that for ...
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I do not see any advantage in this approach whatsoever, nor would I believe, as you suggested, that "many" use this kind of approach.
In fact I find it horribly wrong. Using a single variable (CE in this case) to represent a non-trivial risk-return construct implies the ability to map such relationship to one variable representations. Everybody values risk ...
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There is a great deal of misinformation and out-of-date information on this site. Many of the references in this discussion and elsewhere have serious research flaws.
The Michaud efficient frontier was invented and patented by Robert Michaud and Richard Michaud, U.S. patent # 6,003,018. The alternatives discussed here are not patented nor in many cases ...
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Generally I would annualize risk and returns even when an asset's returns/general time series (ts) does not span over the full year So, both, FB and G present risk and return over the past year. For risk and return that is calculated over longer periods I would not include an asset in the portfolio of which you have no ts available to measure risk and ...
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The term in sample and out of sample are commonly used in any kind of optimization or fitting methods (MVO is just a particular case).
When you make the optimization, you compute optimal parameters (usually the weights of the optimal portfolio in asset allocation) over a given data sample, for example, the returns of the securities of the portfolio for the ...
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I do not think they are directly applicable to MVO because inherently you always model the efficient frontier or asset selection on in-sample data and the result is measured out-of-sample. You can't say, "hey I model it in-sample over 2005 data and then I measure the performance of the portfolio over 2006 data and compare that with results derived from 2010 ...
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The efficient frontier should be expressed in terms of arithmetic returns since only these returns can account for cross-sectional aggregation. Hence, if you assume the log returns of the risky portfolio are $X_{p} \sim N(\mu,\sigma^{2})$, then you first have to convert it to log-normal moments before combining it with the risk-free rate, $r_{f}$. However, ...
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Before answering your questions directly i would like to briefly restate the idea of the resampled efficent frontier:
One of the problem with classical mean variance optimization is (even if the multivariate normal assumption holds) that you cant estimate $\mu$ and $\Omega$ (which is usually denoted as $\Sigma$) exactly. Thats why you incur estimation ...
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First you need to define what you need a risk measure for. It is usually to take a decision, so you have an operational criterion that defines your risk. You should go back at this point and see what is the impact of a change of distribution on it.
Just say for instance that you need a risk measure to take decisions according to a Sharpe ratio and define it ...
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You still want to perform portfolio optimization. Put everything into one bucket, run 'global' portfolio optimization, build the portfolio. Even if you prefer Sharpe ratios, you should do that on the overall portfolio - not just on individual ones. Be careful of sharpe ratios for low risk, low return assets. Dividing one small number by another small number ...
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