Nestle Value Chain Analysis
Developing Robust Asset Allocations1
Working Paper
First Version: February 17, 2006 Current Version: April 18, 2006
Thomas M. Idzorek, CFA Director of Research Ibbotson Associates 225 North Michigan Avenue Suite 700 Chicago, Illinois 60601-7676 312-616-1620 (Main) 312-616-0404 (Fax) tidzorek@ibbotson.com
Abstract
Over the last 50 years, Markowitz’s mean-variance optimization framework has become the asset allocation model of choice. Unfortunately the model often leads to highly concentrated asset allocations, the primary reason that practitioners haven’t fully embraced this Nobel Prize winning idea. Two relatively new techniques that help practitioners develop robust, well-diversified asset allocations are the BlackLitterman model and resampled mean-variance optimization. The first approach focuses on building capital market expectations that behave better within an optimizer while the second approach is an attempt to build a better optimizer. In addition to providing practitioner friendly overviews of the two approaches, this article contributes to the literature by comparing / contrasting empirical examples of both approaches as well as the first empirical example of how the Black-Litterman model and resampled mean-variance optimization can be used together to develop robust asset allocations. Key Words: Robust asset allocation, mean-variance optimization, Black-Litterman, resampling.
© 2006 Ibbotson Associates
Robust Asset Allocation
Page 1
Introduction
In their seminal and extremely influential work, Brinson, Hood, and Beebower [1986] estimates that over time 90% of the variance in returns of a typical portfolio is explained by the variance of the portfolio’s asset allocation. Ibbotson and Kaplan [2000], among others, confirms this important finding supporting the notion that strategic asset allocation (SAA) is the most important decision in the investment process. Strategic asset allocation is both a process and a result. The strategic asset allocation
Working Paper
First Version: February 17, 2006 Current Version: April 18, 2006
Thomas M. Idzorek, CFA Director of Research Ibbotson Associates 225 North Michigan Avenue Suite 700 Chicago, Illinois 60601-7676 312-616-1620 (Main) 312-616-0404 (Fax) tidzorek@ibbotson.com
Abstract
Over the last 50 years, Markowitz’s mean-variance optimization framework has become the asset allocation model of choice. Unfortunately the model often leads to highly concentrated asset allocations, the primary reason that practitioners haven’t fully embraced this Nobel Prize winning idea. Two relatively new techniques that help practitioners develop robust, well-diversified asset allocations are the BlackLitterman model and resampled mean-variance optimization. The first approach focuses on building capital market expectations that behave better within an optimizer while the second approach is an attempt to build a better optimizer. In addition to providing practitioner friendly overviews of the two approaches, this article contributes to the literature by comparing / contrasting empirical examples of both approaches as well as the first empirical example of how the Black-Litterman model and resampled mean-variance optimization can be used together to develop robust asset allocations. Key Words: Robust asset allocation, mean-variance optimization, Black-Litterman, resampling.
© 2006 Ibbotson Associates
Robust Asset Allocation
Page 1
Introduction
In their seminal and extremely influential work, Brinson, Hood, and Beebower [1986] estimates that over time 90% of the variance in returns of a typical portfolio is explained by the variance of the portfolio’s asset allocation. Ibbotson and Kaplan [2000], among others, confirms this important finding supporting the notion that strategic asset allocation (SAA) is the most important decision in the investment process. Strategic asset allocation is both a process and a result. The strategic asset allocation