2021年第7期报告:A New Class of Conditional Haezendonck-Goovaerts Risk Measure

作者: 时间:2021-05-11 点击数:

数据科学学院学术报告 第7期 

 

  目:A New Class of Conditional Haezendonck-Goovaerts Risk Measure

报告人:凌成秀 副教授(西交利物浦大学

  间:2021年5月13号 12:30-13:00

  点:8-425

报告人简介:

凌成秀,副教授,博士(201410月博士毕业于瑞士洛桑大学保险精算专业),20157月回国工作于西南大学数学与统计学院,20192月加入西交利物浦大学科学学院统计与精算学系,从事教学科研工作,担任本科精算专业课程负责人,主要从事极值统计、随机过程的极值分析及风险测度的渐近分析及其在风险管理中的应用,在Extremes, Insurance: Mathematics and Economics, Methodology and Computing in Applied Probability, 中国科学等发表论文20余篇,主持国家自然科学基金(青年项目)一项,中国博士后基金一项,西交利物浦大学博士科研基金及研究发展基金各一项。

报告摘要:

In this talk, we propose a new class of conditional Haezendonck-Goovaerts risk measure. This risk measure highlights how the catastrophic conditional information affects the risk position of the primary risk accordingly.  The basic properties of the proposed risk measures are investigated, including the boundedness, preservation of marginal stop-loss orders, translation invariance, positive homogeneity and subadditivity. Moreover, the first-order condition of this risk measure is given by two inequalities. Several examples are given to illustrate the risk measures and its properties.

 

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