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Robust Bayesian Allocation

Attilio Meucci

ARPM - Advanced Risk and Portfolio Management

May 12, 2011

Using the Bayesian posterior distribution of the market parameters we define self-adjusting uncertainty regions for the robust mean-variance problem. Under a normal-inverse-Wishart conjugate assumption for the market, the ensuing robust Bayesian mean-variance optimal portfolios are shrunk by the aversion to estimation risk toward the global minimum variance portfolio.

After discussing the theory, we test robust Bayesian allocations in a simulation study and in an application to the management of sectors of the S&P 500.

Fully commented code is available for download

Number of Pages in PDF File: 18

Keywords: estimation risk, Bayesian estimation, MCMC, robust optimization, location-dispersion ellipsoid, classical equivalent, shrinkage, global minimum variance portfolio, equally-weighted portfolio, quantitative portfolio management

JEL Classification: C1, G11

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Date posted: April 3, 2005 ; Last revised: May 14, 2011

Suggested Citation

Meucci, Attilio, Robust Bayesian Allocation (May 12, 2011). Available at SSRN: https://ssrn.com/abstract=681553 or http://dx.doi.org/10.2139/ssrn.681553

Contact Information

Attilio Meucci (Contact Author)
ARPM - Advanced Risk and Portfolio Management ( email )
HOME PAGE: http://www.arpm.co/
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