The Intrinsic Logic of the Augmented Black-Litterman Model

Abstract:     
Portfolio management is an art as well as science. We argue that portfolio managers eventually face two fundamental scientific challenges: (a) how to allocate; and (b) how to mimic. These require scientific answers. Other challenges in portfolio management are arts where different tastes, preferences, judgments and beliefs should be allowed. A general portfolio management framework should provide principles for solving the scientific problems, but remain neutral and facilitate interactions with the art part. The Augmented Black-Litterman (ABL) model rightly meets these requirements. This article reveals its intrinsic logic. It features:

- An exploration of the motivation and general requirements of portfolio construction;
- A dissection of the ABL allocation model;
- Three ABL endogenous techniques and their efficiency evidences;
- The intrinsic logic of the ABL model; and
- Justifications of the model as a unified Bayesian allocation framework.

Practitioners can consider this framework a unified allocation theory, which can be used to understand, evaluate and improve existing portfolio construction practices.

Number of Pages in PDF File: 28

Keywords: allocation, factor mimicking, hedging, Augmented Black-Litterman (ABL), Fama-French, factor risk model

JEL Classification: C10, C11, C61, G11

working papers series