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Diversification for infinite-mean heavy-tailed losses without risk aversion

发布日期:2025-06-20点击数:

报告人:胡太忠 教授 (中国科学技术大学)

时间:2025年06月25日 10:00-

地点:数统公司LD402


摘要:In this talk, we introduce the portfolio diversification of infinite mean models. With the notion of majorization order, it is shown that a more diversified portfolio of iid ultra heavy-tailed Pareto losses is larger in the sense of the first-order stochastic dominance. This result is generalized to ultra heavy-tailed Pareto losses which are triggered by catastrophic events, and random losses of which the tails follow an ultra heavy-tailed Pareto distribution. Finally, we introduce a new class of distributions that includes many commonly used heavy-tailed distributions and show that within this class, the above property holds. Remarkably, different from the finite-mean setting, such a diversification benefit does not depend on the decision maker's risk aversion.


邀请人:张志民


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