Multi-asset portfolio optimization and out-of-sample performance: an evaluation of Black–Litterman, mean-variance, and naïve diversification approaches

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. 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 implement the BL model in a multi-asset portfolio context. Using an investment universe of global stock indices, bonds, and commodities, we empirically test the out-of-sample portfolio performance of BL optimized portfolios and compare the results to mean-variance (MV), minimum-variance, and naïve diversified portfolios (1/N-rule) for the period from January 1993 to December 2011. We find that BL optimized portfolios perform better than MV and naïve diversified portfolios in terms of out-of-sample Sharpe ratios even after controlling for different levels of risk aversion, realistic investment constraints, and transaction costs. Interestingly, the BL approach is well suited to alleviate most of the shortcomings of MV optimization. The resulting portfolios are less risky, are more diversified across asset classes, and have less extreme asset allocations. Sensitivity analyses indicate that the outperformance of the BL model is due to the consideration of the reliability of return estimates and a lower portfolio turnover. Terms of use: Documents in

Do commodities add value in multi-asset portfolios? An out-of-sample analysis for different investment strategies

“…In contrast, the Black-Litterman model and the RRT strategy do outperform the naïve strategies providing the highest performance. The superior performance of the Black-Litterman model and the RRT strategy were already reported earlier (Kirby and Ostdiek, 2012;Bessler, Opfer and Wolff, 2015;.…”

“…For this, we combine monetary and stock market signals (Jensen and Mercer, 2003;Bessler, Holler and Kurmann, 2012;Bessler, Opfer and Wolff, 2015). Monetary signals are the first change of the short-term interest rate by the central bank that runs counter to the previous trend.…”

“…We measure the reliability of each return estimate as the variance of the historical forecast errors during the estimation window. The reliability measures are written on the diagonal in the matrix Ω. P is the identity matrix and τ is a parameter that can be used to calibrate the tracking error to the benchmark portfolio (see Bessler, Opfer and Wolff, 2015, for a detailed exposition). The combined return estimate is a matrixweighted average of 'implied' returns and 'views' (Lee, 2000) with respect to the correlation structure.…”

The contribution of the African capital markets in the diversification of investment global portfolios

“…In the second phase, in line with the works of DeMiguel et al (2009), Daskalaki and Skiadopoulos (2011) and Bessler et al (2014, we applied the rolling sample approach, in order to understand the contribution of African assets in the diversification of Europe´s portfolios. This rolling sample methodology consists of considering a window with M observations for a given sub-period.…”

Asset Liability Modelling and Pension Schemes: The Application of Robust Optimization to USS

“…Black-Litterman is the final benchmark and is another way of dealing with estimation errors which combines the subjective views of the investor concerning expected returns and risks with those of a reference portfolio, which is usually the market equilibrium asset proportions and covariance matrix, (Black and Litterman, 1992;Idzorek, 2005;and Bessler, Opfer and Wolff;forthcoming). The resulting (posterior) estimates of expected returns and the covariance matrix are then used in a portfolio model.…”