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Portfolio Rho-Presentativity

Author

Listed:
  • TRISTAN FROIDURE

    (TOBAM, 49-53 Avenue des Champs-Élysées, 75008 Paris, France)

  • KHALID JALALZAI

    (TOBAM, 49-53 Avenue des Champs-Élysées, 75008 Paris, France)

  • YVES CHOUEIFATY

    (TOBAM, 49-53 Avenue des Champs-Élysées, 75008 Paris, France)

Abstract

Given an investment universe, we consider the vector ρ(w) of correlations of all assets to a portfolio with weights w. This vector offers a representation equivalent to w and leads to the notion of ρ-presentative portfolio, that has a positive correlation, or exposure, to all assets. This class encompasses well-known portfolios, and complements the notion of representative portfolio, that has positive amounts invested in all assets (e.g. the market-cap index). We then introduce the concept of maximally ρ-presentative portfolios, that maximize under no particular constraint an aggregate exposure f(ρ(w)) to all assets, as measured by some symmetric, increasing and concave real-valued function f. A basic characterization is established and it is shown that these portfolios are long-only, diversified and form a finite union of polytopes that satisfies a local regularity condition with respect to changes of the covariance matrix of the assets. Despite its small size, this set encompasses many well-known and possibly constrained long-only portfolios, bringing them together in a common framework. This also allowed us characterizing explicitly the impact of maximum weight constraints on the minimum variance portfolio. Finally, several theoretical and numerical applications illustrate our results.

Suggested Citation

  • Tristan Froidure & Khalid Jalalzai & Yves Choueifaty, 2019. "Portfolio Rho-Presentativity," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(07), pages 1-52, November.
  • Handle: RePEc:wsi:ijtafx:v:22:y:2019:i:07:n:s0219024919500341
    DOI: 10.1142/S0219024919500341
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    References listed on IDEAS

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    1. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    2. repec:dau:papers:123456789/4688 is not listed on IDEAS
    3. 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.
    4. Roncalli, Thierry, 2013. "Introduction to Risk Parity and Budgeting," MPRA Paper 47679, University Library of Munich, Germany.
    5. 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.
    6. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    7. 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.
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