Black-Litterman Model

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Portfolio 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.

Distinguished financial engineers Attilio Meucci and Jay Walters offer downloadable implementations of the Black-Litterman method for use with MATLAB® and Financial Toolbox™.

Examples and How To

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, Global Optimization Toolbox