RSS Reading List

My paper on the Black-Litterman Model (Updated 20 June 2014), Accompanying MATLAB codes also on the site

A new spreadsheet which illustrates the differences between the reference models.

A new paper Reconstructing Black-Litterman is now available at SSRN. This paper offers a critique of Michaud et al's recent paper, Deconstructing the Black-Litterman Model, from the Journal of Investment Management.

The author's methods section has been updated with a new taxonomy of the model, and many papers have been added.

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

An applet which implements the Black-Litterman model

What is the Black-Litterman Model

The Black-Litterman model is a model used to estimate inputs for portfolio optimization. It mixes different types of estimates, some based on historical data, others based on equilibrium conditions to arrive at updated estimates. The Mixed Estimation Model was developed by Henri Theil in the early 1960's, but was applied to financial data by Fischer Black and Robert Litterman in the early 1990's.

The beauty of this model is that one can blend a variety of views specified in different ways, absolute or relative, with a given prior estimate to generate a new and updated posterior estimate which includes all the views. The diagram below shows what the mixing might look like in a single dimension. The updated posterior estimate should be centered more closely around the unknown mean, and should also have a lower variance(higher precision) that either the prior or conditional distribution.

Black-Litterman Model

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