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A Generalized Black–Litterman Model

Author

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  • Shea D. Chen

    (Eyon Holding Group, Taipei 10483, Taiwan)

  • Andrew E. B. Lim

    (Department of Analytics and Operations, Department of Finance, and Institute for Operations Research and Analytics, National University of Singapore, 119245 Singapore)

Abstract

The Black–Litterman model provides a framework for combining the forecasts of a backward-looking equilibrium model with the views of (several) forward-looking experts in a portfolio allocation decision. The classical version uses the capital asset pricing model to specify expected returns, and assumes that expert views are unbiased noisy observations of future returns. It combines the two using Bayes’ rule and the portfolio allocation decision is made on the basis of the updated forecast. The classical Black–Litterman model assumes that the equilibrium and expert models are accurately specified. This is generally not the case, however, and there may be substantial efficiency loss if misspecification is ignored. In this paper, we formulate a generalized Black–Litterman model that accounts for both misspecification and bias in the equilibrium and expert models. We show how to calibrate this model using historical view and return data, and study the value of our generalized model for portfolio construction. More generally, this paper shows how the views of multiple experts can be modeled as a Bayesian graphical model and estimated using historical data, which may be of interest in applications that involve the aggregation of expert opinions for the purpose of decision making.

Suggested Citation

  • Shea D. Chen & Andrew E. B. Lim, 2020. "A Generalized Black–Litterman Model," Operations Research, INFORMS, vol. 68(2), pages 381-410, March.
    • Handle: RePEc:inm:oropre:v:68:y:2020:i:2:p:381-410
    DOI: 10.1287/opre.2019.1893
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    References listed on IDEAS

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    1. Khandani, Amir E. & Lo, Andrew W., 2011. "What happened to the quants in August 2007? Evidence from factors and transactions data," Journal of Financial Markets, Elsevier, vol. 14(1), pages 1-46, February.
    2. Victor DeMiguel & Lorenzo Garlappi & Francisco J. Nogales & Raman Uppal, 2009. "A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms," Management Science, INFORMS, vol. 55(5), pages 798-812, May.
    3. Fama, Eugene F & French, Kenneth R, 1996. "Multifactor Explanations of Asset Pricing Anomalies," Journal of Finance, American Finance Association, vol. 51(1), pages 55-84, March.
    4. John Douglas (J.D.) Opdyke, 2007. "Comparing Sharpe ratios: So where are the p-values?," Journal of Asset Management, Palgrave Macmillan, vol. 8(5), pages 308-336, December.
    5. S Satchell & A Scowcroft, 2000. "A demystification of the Black–Litterman model: Managing quantitative and traditional portfolio construction," Journal of Asset Management, Palgrave Macmillan, vol. 1(2), pages 138-150, September.
    6. Steven Beach & Alexei Orlov, 2007. "An application of the Black–Litterman model with EGARCH-M-derived views for international portfolio management," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 21(2), pages 147-166, June.
    7. Dimitris Bertsimas & Vishal Gupta & Ioannis Ch. Paschalidis, 2012. "Inverse Optimization: A New Perspective on the Black-Litterman Model," Operations Research, INFORMS, vol. 60(6), pages 1389-1403, December.
    8. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    9. Eugene F. Fama & Kenneth R. French, 2004. "The Capital Asset Pricing Model: Theory and Evidence," Journal of Economic Perspectives, American Economic Association, vol. 18(3), pages 25-46, Summer.
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