Black-Litterman Model

Porfolio analysis with MATLAB using the Black-Litterman model

The Black-Litterman model extends traditional portfolio theory by incorporating investors’ views to arrive at a bespoke asset allocation.

Developed by Fisher Black and Bob Litterman in the 1990s, the Black-Litterman model uses mixed estimation techniques to combine the market equilibrium vector of expected returns with an investor-specific, usually Bayesian-derived, vector to form a new, posterior estimate of expected returns. The final vector of expected returns is assumed to have a probability distribution of the product of two multivariate normal distributions.

To overcome the limitations in modern portfolio theory, many asset management companies have adopted the Black-Litterman model to implement practical asset allocation models.

You can use MATLAB^® to implement the Black-Litterman model. Sample implementations and extensions are available for download from practitioners such as Attilio Meucci and Jay Walters. You can use these functions with the Financial Toolbox™ portfolio analysis and asset allocation functionality.

Examples and How To

• Risk and Asset Allocation Files from Attilio Meucci (Downloadable Functions and Scripts)
• Generating Code for Portfolio Optimization (Black-Litterman Approach) (Example)
• Using MATLAB to Develop Portfolio Optimization Models (Webinar)
• Build a Portfolio Analysis Production Application (Webinar)

Software Reference

• Portfolio Class – Portfolio object for mean-variance portfolio optimization and analysis (Function)
• Portalloc – Optimal capital allocation to efficient frontier portfolios (Function)
• Portcons – Portfolio constraints (Function)
• Portstats – Portfolio expected return and risk (Function)

See also: portfolio optimization, CAPM, financial risk management, investment management, portfolio optimization and analysis