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Notions of multivariate dependence and their applications in optimal portfolio selections with dependent risks

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  • Cai, Jun
  • Wei, Wei

Abstract

In this paper, we propose the dependence notions of weakly stochastic arrangement increasing through left tail probability (LWSAI) and weakly stochastic arrangement increasing (WSAI) to model multivariate dependent risks. We derive properties and characterizations of these new notions and show that many existing dependence structures are the special cases of these notions of dependence. We apply the dependence notions of LWSAI and WSAI to the problem of optimal portfolio selections with dependent risks and generalize many existing studies.

Suggested Citation

  • Cai, Jun & Wei, Wei, 2015. "Notions of multivariate dependence and their applications in optimal portfolio selections with dependent risks," Journal of Multivariate Analysis, Elsevier, vol. 138(C), pages 156-169.
    • Handle: RePEc:eee:jmvana:v:138:y:2015:i:c:p:156-169
    DOI: 10.1016/j.jmva.2014.12.011
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Wei Wei, 2019. "Single machine scheduling with stochastically dependent times," Journal of Scheduling, Springer, vol. 22(6), pages 677-689, December.
    2. Li, Chen & Li, Xiaohu, 2020. "Preservation of weak SAI’s under increasing transformations with applications," Statistics & Probability Letters, Elsevier, vol. 164(C).
    3. Wei, Wei, 2017. "Joint stochastic orders of high degrees and their applications in portfolio selections," Insurance: Mathematics and Economics, Elsevier, vol. 76(C), pages 141-148.
    4. Li, Chen & Li, Xiaohu, 2017. "Preservation of weak stochastic arrangement increasing under fixed time left-censoring," Statistics & Probability Letters, Elsevier, vol. 129(C), pages 42-49.
    5. Yiying Zhang & Weiyong Ding & Peng Zhao, 2018. "On total capacity of k‐out‐of‐n systems with random weights," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(4), pages 347-359, June.
    6. Li, Chen & Li, Xiaohu, 2019. "Preservation of WSAI under default transforms and its application in allocating assets with dependent realizable returns," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 84-91.
    7. Yinping You & Xiaohu Li & Rui Fang, 2021. "On coverage limits and deductibles for SAI loss severities," Annals of Operations Research, Springer, vol. 297(1), pages 341-357, February.
    8. Hossein Nadeb & Hamzeh Torabi & Ali Dolati, 2018. "Stochastic comparisons of the largest claim amounts from two sets of interdependent heterogeneous portfolios," Papers 1812.08343, arXiv.org.
    9. Zhang, Yiying & Cheung, Ka Chun, 2020. "On the increasing convex order of generalized aggregation of dependent random variables," Insurance: Mathematics and Economics, Elsevier, vol. 92(C), pages 61-69.
    10. Li, Chen & Li, Xiaohu, 2017. "Ordering optimal deductible allocations for stochastic arrangement increasing risks," Insurance: Mathematics and Economics, Elsevier, vol. 73(C), pages 31-40.
    11. Wei Wei, 2018. "Properties of Stochastic Arrangement Increasing and Their Applications in Allocation Problems," Risks, MDPI, vol. 6(2), pages 1-12, April.
    12. Li, Xiaohu & Li, Chen, 2016. "On allocations to portfolios of assets with statistically dependent potential risk returns," Insurance: Mathematics and Economics, Elsevier, vol. 68(C), pages 178-186.
    13. Pan Xiaoqing & Li Xiaohu, 2017. "On capital allocation for stochastic arrangement increasing actuarial risks," Dependence Modeling, De Gruyter, vol. 5(1), pages 145-153, January.
    14. You, Yinping & Li, Xiaohu, 2015. "Functional characterizations of bivariate weak SAI with an application," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 225-231.
    15. Li, Chen & Li, Xiaohu, 2016. "Sufficient conditions for ordering aggregate heterogeneous random claim amounts," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 406-413.
    16. Ju, Shan & Pan, Xiaoqing, 2016. "A new proof for the peakedness of linear combinations of random variables," Statistics & Probability Letters, Elsevier, vol. 114(C), pages 93-98.

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