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General lower bounds on convex functionals of aggregate sums

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  • Cheung, Ka Chun
  • Lo, Ambrose

Abstract

The determination of the dependence structure giving rise to the minimal convex sum in a general Fréchet space is a practical, yet challenging problem in quantitative risk management. In this article, we consider the closely related problem of finding lower bounds on three kinds of convex functionals, namely, convex expectations, Tail Value-at-Risk and the Haezendonck–Goovaerts risk measure, of a sum of random variables with arbitrary distributions. The sharpness of the lower bounds on the first two types of convex functionals is characterized via the extreme negative dependence structure of mutual exclusivity. Compared to existing results in the literature, our new lower bounds enjoy the advantages of generality and analytic tractability.

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  • Cheung, Ka Chun & Lo, Ambrose, 2013. "General lower bounds on convex functionals of aggregate sums," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 884-896.
    • Handle: RePEc:eee:insuma:v:53:y:2013:i:3:p:884-896
    DOI: 10.1016/j.insmatheco.2013.10.005
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    Cited by:

    1. Ka Chun Cheung & Michel Denuit & Jan Dhaene, 2017. "Tail mutual exclusivity and Tail-VaR lower bounds," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2017(1), pages 88-104, January.
    2. Chuancun Yin & Dan Zhu, 2016. "Sharp convex bounds on the aggregate sums--An alternative proof," Papers 1603.05373, arXiv.org, revised May 2016.
    3. Xun, Li & Zhou, Yangzhi & Zhou, Yong, 2019. "A generalization of Expected Shortfall based capital allocation," Statistics & Probability Letters, Elsevier, vol. 146(C), pages 193-199.
    4. Liu, Qing & Peng, Liang & Wang, Xing, 2017. "Haezendonck–Goovaerts risk measure with a heavy tailed loss," Insurance: Mathematics and Economics, Elsevier, vol. 76(C), pages 28-47.
    5. Tang, Qihe & Yang, Fan, 2014. "Extreme value analysis of the Haezendonck–Goovaerts risk measure with a general Young function," Insurance: Mathematics and Economics, Elsevier, vol. 59(C), pages 311-320.
    6. Lux, Thibaut & Papapantoleon, Antonis, 2019. "Model-free bounds on Value-at-Risk using extreme value information and statistical distances," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 73-83.
    7. Wang, Xing & Peng, Liang, 2016. "Inference for intermediate Haezendonck–Goovaerts risk measure," Insurance: Mathematics and Economics, Elsevier, vol. 68(C), pages 231-240.
    8. Chaoubi, Ihsan & Cossette, Hélène & Gadoury, Simon-Pierre & Marceau, Etienne, 2020. "On sums of two counter-monotonic risks," Insurance: Mathematics and Economics, Elsevier, vol. 92(C), pages 47-60.
    9. Hanbali, Hamza & Dhaene, Jan & Linders, Daniël, 2022. "Dependence bounds for the difference of stop-loss payoffs on the difference of two random variables," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 22-37.
    10. Chuancun Yin & Dan Zhu, 2016. "Sharp Convex Bounds on the Aggregate Sums–An Alternative Proof," Risks, MDPI, vol. 4(4), pages 1-8, September.
    11. Paul Embrechts & Bin Wang & Ruodu Wang, 2015. "Aggregation-robustness and model uncertainty of regulatory risk measures," Finance and Stochastics, Springer, vol. 19(4), pages 763-790, October.
    12. Cheung, Ka Chun & Lo, Ambrose, 2014. "Characterizing mutual exclusivity as the strongest negative multivariate dependence structure," Insurance: Mathematics and Economics, Elsevier, vol. 55(C), pages 180-190.

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