A Theoretical and Empirical Analysis of the Black-Litterman Model
The Black-Litterman (BL) model aims to enhance asset allocation decisions by overcoming the weaknesses of standard mean-variance (MV) portfolio optimization. In this study we propose a method that enables the implementation of the BL model on a multi-asset portfolio allocation decision. Further, we empirically test the out-of-sample portfolio performance of BL optimized portfolios in comparison to mean-variance (MV), minimum-variance, and adequate benchmark portfolios. Using an investment universe of global stock markets, bonds, and commodities, we find that for the period from January 2000 to August 2011 out-of-sample BL optimized portfolios provide superior Sharpe ratios, even after controlling for different levels of risk aversion, realistic investment constraints, and transaction costs. Further the BL approach is suitable to alleviate most of the shortcomings of MV optimization, in that the resulting portfolios are more diversified, less extreme, and hence, economically more intuitive.
KeywordsSharpe Ratio Asset Class Portfolio Weight Return Estimate Portfolio Allocation
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