Beyond Black-Litterman: Letting the Data Speak

Posted: 29 Apr 2008 Last revised: 1 Jun 2009

See all articles by Guofu Zhou

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School; China Academy of Financial Research (CAFR)

Date Written: April 1, 2008

Abstract

The Black-Litterman model is a popular approach for asset allocation by blending an investor's proprietary views with the views of the market. However, their model ignores the data-generating process whose dynamics can have significant impact on future portfolio returns. This paper extends the Black-Litterman model to allow Bayesian learning to exploit all available information - the market views, the investor's proprietary views as well as the data. The framework allows practitioners to combine insights from the Black-Litterman model with the data to generate potentially more reliable trading strategies and more robust portfolios.

Further, we show that many Bayesian learning tools can now be readily applied to practical portfolio selections in conjunction with the Black-Litterman model.

Keywords: Black-Litterman, Bayesian, Mean-variance, Portfolio Choice, Views

Suggested Citation

Zhou, Guofu, Beyond Black-Litterman: Letting the Data Speak (April 1, 2008). Available at SSRN: https://ssrn.com/abstract=1125282

Guofu Zhou (Contact Author)

Washington University in St. Louis - John M. Olin Business School ( email )

Washington University
Campus Box 1133
St. Louis, MO 63130-4899
United States
314-935-6384 (Phone)
314-658-6359 (Fax)

HOME PAGE: http://apps.olin.wustl.edu/faculty/zhou/

China Academy of Financial Research (CAFR)

Shanghai Advanced Institute of Finance
Shanghai P.R.China, 200030
China

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