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Almost Marginal Conditional Stochastic Dominance

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  • Denuit, Michel
  • Huang, Rachel
  • Tzeng, Larry

Abstract

Marginal Conditional Stochastic Dominance (MCSD) developed by Shalit and Yitzhaki (1994) gives the conditions under which all risk-averse individuals prefer to increase the share of one risky asset over another in a given portfolio. In this paper, we extend this concept to provide conditions under which most (and not all) risk-averse investors behave in this way. Instead of stochastic dominance rules, almost stochastic dominance is used to assess the superiority of one asset over another in a given portfolio. Switching from MCSD to Almost MCSD (AMCSD) helps to reconcile common practices in asset allocation and the decision rules supporting stochastic dominance relations. A financial application is further provided to demonstrate that using AMCSD can indeed improve investment efficiency.
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Suggested Citation

  • Denuit, Michel & Huang, Rachel & Tzeng, Larry, 2012. "Almost Marginal Conditional Stochastic Dominance," LIDAM Discussion Papers ISBA 2012033, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvad:2012033
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    14. Larry Y. Tzeng & Rachel J. Huang & Pai-Ta Shih, 2013. "Revisiting Almost Second-Degree Stochastic Dominance," Management Science, INFORMS, vol. 59(5), pages 1250-1254, May.
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    Cited by:

    1. Gleb Gersman & Haim Shalit, 2014. "Optimizing MCSD Portfolios," Working Papers 1410, Ben-Gurion University of the Negev, Department of Economics.
    2. Bruni, Renato & Cesarone, Francesco & Scozzari, Andrea & Tardella, Fabio, 2017. "On exact and approximate stochastic dominance strategies for portfolio selection," European Journal of Operational Research, Elsevier, vol. 259(1), pages 322-329.
    3. Wei-Han Liu & Jow-Ran Chang & Guo-Jun Yang, 2024. "An improved criterion for almost marginal conditional stochastic dominance," Review of Quantitative Finance and Accounting, Springer, vol. 62(3), pages 1251-1290, April.
    4. Heuchenne, Cédric & Jacquemain, Alexandre, 2022. "Inference for monotone single-index conditional means: A Lorenz regression approach," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    5. Francesco Cesarone & Justo Puerto, 2024. "New approximate stochastic dominance approaches for Enhanced Indexation models," Papers 2401.12669, arXiv.org.
    6. David Cerezo S'anchez, 2022. "Zero-Knowledge Optimal Monetary Policy under Stochastic Dominance," Papers 2210.06139, arXiv.org.
    7. Chen, Tzu-Ying & Tsai, An-Mei & Tzeng, Larry Y., 2022. "Revisiting almost marginal conditional stochastic dominance," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 260-269.

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    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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