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On the Computation of Optimal Monotone Mean-Variance Portfolios via Truncated Quadratic Utility

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  • Ales Cerný
  • Fabio Maccheroni
  • Massimo Marinacci
  • Aldo Rustichini

Abstract

We report a surprising link between optimal portfolios generated by a special type of variational preferences called divergence preferences (cf. [8]) and optimal portfolios generated by classical expected utility. As a special case we connect optimization of truncated quadratic utility (cf. [2]) to the optimal monotone mean-variance portfolios (cf. [9]), thus simplifying the computation of the latter.

Suggested Citation

  • Ales Cerný & Fabio Maccheroni & Massimo Marinacci & Aldo Rustichini, 2008. "On the Computation of Optimal Monotone Mean-Variance Portfolios via Truncated Quadratic Utility," Carlo Alberto Notebooks 79, Collegio Carlo Alberto.
    • Handle: RePEc:cca:wpaper:79
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    References listed on IDEAS

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    1. Frittelli, Marco & Rosazza Gianin, Emanuela, 2002. "Putting order in risk measures," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1473-1486, July.
    2. Fabio Maccheroni & Massimo Marinacci & Aldo Rustichini, 2006. "Ambiguity Aversion, Robustness, and the Variational Representation of Preferences," Econometrica, Econometric Society, vol. 74(6), pages 1447-1498, November.
    3. Cern›, Ales, 2002. "Generalized Sharpe Ratios and Asset Pricing in Incomplete Markets," Royal Economic Society Annual Conference 2002 41, Royal Economic Society.
    4. Fabio Maccheroni & Massimo Marinacci & Aldo Rustichini & Marco Taboga, 2009. "Portfolio Selection With Monotone Mean‐Variance Preferences," Mathematical Finance, Wiley Blackwell, vol. 19(3), pages 487-521, July.
    5. Filipovic, Damir & Kupper, Michael, 2007. "Monotone and cash-invariant convex functions and hulls," Insurance: Mathematics and Economics, Elsevier, vol. 41(1), pages 1-16, July.
    6. Aharon Ben‐Tal & Marc Teboulle, 2007. "An Old‐New Concept Of Convex Risk Measures: The Optimized Certainty Equivalent," Mathematical Finance, Wiley Blackwell, vol. 17(3), pages 449-476, July.
    7. Aleš Černý & Jan Kallsen, 2008. "Mean–Variance Hedging And Optimal Investment In Heston'S Model With Correlation," Mathematical Finance, Wiley Blackwell, vol. 18(3), pages 473-492, July.
    8. Hanqing Jin & Harry Markowitz & Xun Yu Zhou, 2006. "A Note On Semivariance," Mathematical Finance, Wiley Blackwell, vol. 16(1), pages 53-61, January.
    9. Hans Föllmer & Alexander Schied, 2002. "Convex measures of risk and trading constraints," Finance and Stochastics, Springer, vol. 6(4), pages 429-447.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Éric André, 2014. "Crisp Fair Gambles," AMSE Working Papers 1410, Aix-Marseille School of Economics, France, revised 15 Mar 2014.
    2. Fabio Maccheroni & Massimo Marinacci & Aldo Rustichini & Marco Taboga, 2009. "Portfolio Selection With Monotone Mean‐Variance Preferences," Mathematical Finance, Wiley Blackwell, vol. 19(3), pages 487-521, July.
    3. Alev{s} v{C}ern'y, 2019. "Semimartingale theory of monotone mean--variance portfolio allocation," Papers 1903.06912, arXiv.org, revised Jan 2020.
    4. Samuel Drapeau & Michael Kupper & Antonis Papapantoleon, 2012. "A Fourier Approach to the Computation of CV@R and Optimized Certainty Equivalents," Papers 1212.6732, arXiv.org, revised Dec 2013.
    5. Yang Shen & Bin Zou, 2022. "Cone-constrained Monotone Mean-Variance Portfolio Selection Under Diffusion Models," Papers 2205.15905, arXiv.org.

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    More about this item

    Keywords

    optimal portfolio; truncated quadratic utility; monotone mean-variance preferences; divergence preferences; HARA utility;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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