Multi-Asset Portfolio Optimization and Out-of-Sample Performance: An Evaluation of Black-Litterman, Mean Variance and Naïve Diversification Approaches
European Journal of Finance, Forthcoming.
48 Pages Posted: 12 Jun 2012 Last revised: 6 Dec 2014
Date Written: December 2014
The Black-Litterman model aims to enhance asset allocation decisions by overcoming the problems of mean-variance portfolio optimization. We propose a sample based version of the Black-Litterman model and implement it on a multi-asset portfolio consisting of global stocks, bonds, and commodity indices, covering the period from January 1993 to December 2011. We test its out-of-sample performance relative to other asset allocation models and find that Black-Litterman optimized portfolios significantly outperform naïve-diversified portfolios (1/N-rule and strategic weights), and consistently perform better than mean-variance, Bayes-Stein, and minimum-variance strategies in terms of out-of-sample Sharpe ratios, even after controlling for different levels of risk aversion, investment constraints, and transaction costs. The BL model generates portfolios with lower risk, less extreme asset allocations, and higher diversification across asset classes. Sensitivity analyses indicate that these advantages are due to more stable mixed return estimates that incorporate the reliability of return predictions, smaller estimation errors, and lower turnover.
Keywords: Portfolio Optimization, Black-Litterman, Mean-Variance, Minimum Variance, Bayes-Stein, Naïve diversification, 1/N, Markowitz
JEL Classification: C61, G11
Suggested Citation: Suggested Citation