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Confidence in prior knowledge: Calibration and impact on portfolio performance

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  • Wickern, Tobias

Abstract

The specification of prior parameters is a common practical problem when implementing Bayesian approaches to portfolio optimization. The precision parameter of the prior on the expected asset returns reflects the confidence of the investor in the prior knowledge. Within the framework of the normal-inverse-Wishart model, this paper investigates which factors drive this confidence in order to deduce reasonable values of the precision parameter. We recommend that the investor concentrates on the specification of the precision parameter. By contrast, experts should assess the values of the prior location and dispersion parameter. In the second part of the paper, the impact of the investor's confidence on the performance of investment strategies is examined by a simulation study. The study focusses less on detecting superior portfolio strategies, and more on providing a sensitivity analysis for different levels of confidence. In addition, we show how the posterior distribution of the normal-inverse-Wishart model can be used as a starting point of a simulation process.

Suggested Citation

  • Wickern, Tobias, 2011. "Confidence in prior knowledge: Calibration and impact on portfolio performance," Discussion Papers in Econometrics and Statistics 7/11, University of Cologne, Institute of Econometrics and Statistics.
    • Handle: RePEc:zbw:ucdpse:711
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    1. Ravi Jagannathan & Tongshu Ma, 2003. "Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps," Journal of Finance, American Finance Association, vol. 58(4), pages 1651-1683, August.
    2. Cogley, Timothy & Sargent, Thomas J., 2008. "The market price of risk and the equity premium: A legacy of the Great Depression?," Journal of Monetary Economics, Elsevier, vol. 55(3), pages 454-476, April.
    3. Kan, Raymond & Zhou, Guofu, 2007. "Optimal Portfolio Choice with Parameter Uncertainty," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 42(3), pages 621-656, September.
    4. 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.
    5. Gabriel Frahm, 2010. "Linear statistical inference for global and local minimum variance portfolios," Statistical Papers, Springer, vol. 51(4), pages 789-812, December.
    6. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    7. Nikolay Gospodinov & Raymond Kan & Cesare Robotti, 2010. "On the Hansen-Jagannathan distance with a no-arbitrage constraint," FRB Atlanta Working Paper 2010-04, Federal Reserve Bank of Atlanta.
    8. Kalymon, Basil A., 1971. "Estimation Risk in the Portfolio Selection Model," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 6(1), pages 559-582, January.
    9. Raymond Kan & Cesare Robotti, 2008. "The exact distribution of the Hansen-Jagannathan bound," FRB Atlanta Working Paper 2008-09, Federal Reserve Bank of Atlanta.
    10. Jobson, J D & Korkie, Bob, 1984. "On the Jensen Measure and Marginal Improvements in Portfolio Performance: A Note," Journal of Finance, American Finance Association, vol. 39(1), pages 245-251, March.
    11. Ledoit, Olivier & Wolf, Michael, 2003. "Improved estimation of the covariance matrix of stock returns with an application to portfolio selection," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 603-621, December.
    12. Ravi Jagannathan & Tongshu Ma, 2003. "Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps," Journal of Finance, American Finance Association, vol. 58(4), pages 1651-1684, August.
    13. Elroy Dimson & Paul Marsh & Mike Staunton, 2003. "Global Evidence On The Equity Risk Premium," Journal of Applied Corporate Finance, Morgan Stanley, vol. 15(4), pages 27-38, September.
    14. Merton, Robert C., 1980. "On estimating the expected return on the market : An exploratory investigation," Journal of Financial Economics, Elsevier, vol. 8(4), pages 323-361, December.
    15. J. Tobin, 1958. "Liquidity Preference as Behavior Towards Risk," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 25(2), pages 65-86.
    16. Jorion, Philippe, 1986. "Bayes-Stein Estimation for Portfolio Analysis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 21(3), pages 279-292, September.
    17. Jorion, Philippe, 1991. "Bayesian and CAPM estimators of the means: Implications for portfolio selection," Journal of Banking & Finance, Elsevier, vol. 15(3), pages 717-727, June.
    18. Best, Michael J & Grauer, Robert R, 1991. "On the Sensitivity of Mean-Variance-Efficient Portfolios to Changes in Asset Means: Some Analytical and Computational Results," Review of Financial Studies, Society for Financial Studies, vol. 4(2), pages 315-342.
    19. Satchell, Stephen, 2007. "Forecasting Expected Returns in the Financial Markets," Elsevier Monographs, Elsevier, edition 1, number 9780750683210.
    20. Brennan, Michael J. & Chordia, Tarun & Subrahmanyam, Avanidhar, 1998. "Alternative factor specifications, security characteristics, and the cross-section of expected stock returns," Journal of Financial Economics, Elsevier, vol. 49(3), pages 345-373, September.
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    More about this item

    Keywords

    Bayesian portfolio optimization; Conjugate prior; Confidence parameter; Normal-inverse-Wishart model; Tangency portfolio; Markowitz; Sharpe ratio; Sensitivity analysis; Simulation study;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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