IDEAS home Printed from https://ideas.repec.org/p/aiz/louvar/2014003.html

Almost marginal conditional stochastic dominance

Author

Listed:
  • Denuit, Michel
  • Huang, Rachel J.
  • Wang, Christine

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.
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Denuit, Michel & Huang, Rachel J. & Wang, Christine, 2014. "Almost marginal conditional stochastic dominance," LIDAM Reprints ISBA 2014003, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2014003
    Note: In : Journal of Banking & Finance, vol. 41, p. 57-66 (2014)
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gleb Gersman & Haim Shalit, 2014. "Optimizing MCSD Portfolios," Working Papers 1410, Ben-Gurion University of the Negev, Department of Economics.
    2. Heuchenne, Cédric & Jacquemain, Alexandre, 2022. "Inference for monotone single-index conditional means: A Lorenz regression approach," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    3. 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.
    4. Cesarone, Francesco & Puerto, Justo, 2025. "Flexible enhanced indexation models through stochastic dominance and ordered weighted average optimization," European Journal of Operational Research, Elsevier, vol. 323(2), pages 657-670.
    5. David Cerezo S'anchez, 2022. "Zero-Knowledge Optimal Monetary Policy under Stochastic Dominance," Papers 2210.06139, arXiv.org.
    6. Francesco Cesarone & Justo Puerto, 2024. "New approximate stochastic dominance approaches for Enhanced Indexation models," Papers 2401.12669, arXiv.org.
    7. 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.
    8. 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.

    More about this item

    JEL classification:

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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aiz:louvar:2014003. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Nadja Peiffer (email available below). General contact details of provider: https://edirc.repec.org/data/isuclbe.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.