blacklitterman.org

Comparison of Author's Methods

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A new implementation of the Black-Litterman model in Excel is available on the implementations page.

An implementation of the Black-Litterman model in python and the worked example from the He and Litterman 1999 paper (Updated Jun 22 2012)

An excel spreadsheet showing the example worked in the He and Litterman paper (Updated Jun 26 2012)

New paper focusing on Tau and if you really need it (Updated 1 November 2010)

MATLAB and SciLAB implementations of the model

My paper on the Black-Litterman Model (Updated 16 February 2009)

An applet which implements the Black-Litterman model

The table below attempts to summarize some of the differences between the various authors on several dimensions where the author's tend to disagree.

Author(s) τ View Uncertainty Posterior Variance
Fuasi and Meucci Ignores (1) αPΣP, α ≥ 1 Use prior variance
He and Litterman Close to 0 diag(τPΣP) Updated
Idzorek Close to 0 Specified as % Use prior variance
Satchell and Scowcroft Usually 1 ? Use prior variance

Description of the various attributes in the table

τ  -  The authors either expect that τ = 1, or else it is a small number. In general an investor will have τ << 1 if they are going to update the variamce, and will use τ = 1 if they are not going to update the variance. Some authors (not included in the references on this site) also discuss using values of τ > 1. Given what τ&SIGMA; represents (uncertainty in our estimate of the mean) and the fact that τ greater than 1 does not make sense in this context, I've not added those views to the table. See or my paper for further information

View Uncertainty - The authors use various expressions to specify the uncertainty of the views. Fusasi and Meucci specify that the uncertainty of the views will be a multiple of the uncertainty of the prior distribution. He and Litterman specify the view uncertainty as a diagonal matrix, with on-diagonal elements equal to the uncertainty of the prior distribution.

Posterior Variance - The authors either update the variance based on the variance of the posterior distribution, or else they just use the prior variance of returns.

For a more thorough discussion see my paper on the Black-Litterman model.

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