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3

Of course you can choose the prior. As far as I understand the literature, the BL-model is characterized by using the equilibrium implied returns. Otherwise it would just be a Bayesian model. If you estimate the returns in a different way (not taking implied returns from the market portfolio), you could lose the stabilizing inverse optimization step ...


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Jay Walter's Paper on "The Factor Tau in the Black-Litterman Model" http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1701467 is also useful to review


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I don't believe there is one generally accepted method and a number of papers are written on this issue. The Black-Litterman Approach: Original Model and Extensions (2008) by Meucci has an overview and I believe is generally useful to learn more. It suggests using $\tau = \frac{1}{T}$ but notes more complicated approaches exist. A demystification of the ...


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It depends on your investment process: more specifically, on how you generate views. Here are three practical cases which lead to different choices for $\Omega$: Let's assume you are an investor who acts on (more or less) arbitrary bits of opinion: e.g. you like Italian equities because you like Italy, and German equities because you find Angela Merkel's ...


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In The Black-Litterman Model In Detail Jay Walters says the following on p. 13 (top paragraph): First, by construction we will require each view to be unique and uncorrelated with the other views. This will give the conditional distribution the property that the covariance matrix will be diagonal, with all offdiagonal entries equal to 0. We constrain the ...


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When I implemented a BL model, I chose to do the omega optimization using the technique Idzorek proposed here: https://corporate.morningstar.com/ib/documents/MethodologyDocuments/IBBAssociates/BlackLitterman.pdf It's a numerical procedure though.


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In practice, $\Omega$ (the covariance of the investor views) often 'inherits' the market covariance $\Sigma$. A convenient choice is $ \Omega = \left( 1/c -1 \right) P \Sigma P^T$ where $c$ is a confidence parameter: the case $c \rightarrow 1$ corresponds to a strongly peaked distribution of views (the investor views dominate the market), while $c ...


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There is also http://www.blacklitterman.org/ Where you can find an implementation under Excel and Matlab of the Model.



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