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Generalized Almost Stochastic Dominance

Author

Listed:
  • Ilia Tsetlin

    () (INSEAD, Singapore 138676)

  • Robert L. Winkler

    () (Fuqua School of Business, Duke University, Durham, North Carolina 27708)

  • Rachel J. Huang

    () (Department of Finance, National Central University, Jhongli City, Taoyuan County 32001, Taiwan)

  • Larry Y. Tzeng

    () (Department of Finance, National Taiwan University, Taipei 106, Taiwan)

Abstract

Almost stochastic dominance allows small violations of stochastic dominance rules to avoid situations where most decision makers prefer one alternative to another but stochastic dominance cannot rank them. While the idea behind almost stochastic dominance is quite promising, it has not caught on in practice. Implementation issues and inconsistencies between integral conditions and their associated utility classes contribute to this situation. We develop generalized almost second-degree stochastic dominance and almost second-degree risk in terms of the appropriate utility classes and their corresponding integral conditions, and extend these concepts to higher degrees. We address implementation issues and show that generalized almost stochastic dominance inherits the appealing properties of stochastic dominance. Finally, we define convex generalized almost stochastic dominance to deal with risk-prone preferences. Generalized almost stochastic dominance could be useful in decision analysis, empirical research (e.g., in finance), and theoretical analyses of applied situations.

Suggested Citation

  • Ilia Tsetlin & Robert L. Winkler & Rachel J. Huang & Larry Y. Tzeng, 2015. "Generalized Almost Stochastic Dominance," Operations Research, INFORMS, vol. 63(2), pages 363-377, April.
  • Handle: RePEc:inm:oropre:v:63:y:2015:i:2:p:363-377
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    File URL: http://dx.doi.org/10.1287/opre.2014.1340
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    Citations

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    Cited by:

    1. repec:zbw:ifweej:201714 is not listed on IDEAS
    2. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Management Information, Decision Sciences, and Financial Economics: A Connection," Tinbergen Institute Discussion Papers 18-004/III, Tinbergen Institute.
    3. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Decision Sciences, Economics, Finance, Business, Computing, and Big Data: Connections," Tinbergen Institute Discussion Papers 18-024/III, Tinbergen Institute.
    4. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 11(1), pages 1-29, March.
    5. repec:spr:sochwe:v:51:y:2018:i:1:d:10.1007_s00355-017-1106-0 is not listed on IDEAS
    6. repec:zbw:ifweej:201851 is not listed on IDEAS
    7. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2016. "Management science, economics and finance: A connection," Documentos de Trabajo del ICAE 2016-07, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    8. Yunna Wu & Chuanbo Xu & Hu Xu, 2016. "Optimal Site Selection of Tidal Power Plants Using a Novel Method: A Case in China," Energies, MDPI, Open Access Journal, vol. 9(10), pages 1-26, October.
    9. Gao, Jianwei & Zhao, Feng, 2017. "Sufficient conditions of stochastic dominance for general transformations and its application in option strategy," Economics Discussion Papers 2017-40, Kiel Institute for the World Economy (IfW).
    10. Zhao, Feng & Gao, Jianwei & Gu, Yundong, 2018. "Almost first-degree stochastic dominance for transformations and its application in insurance strategy," Economics Discussion Papers 2018-46, Kiel Institute for the World Economy (IfW).
    11. repec:eee:ejores:v:264:y:2018:i:2:p:675-685 is not listed on IDEAS
    12. repec:eee:insuma:v:78:y:2018:i:c:p:255-266 is not listed on IDEAS

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