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模拟不确定性-我们的尾部统计数据有多可靠?.pdf

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1、SIMULATION UNCERTAINTYHow Reliable are Our Tail Statistics?June 2023Dean Marcus,FCAS CERA|SVP Actuary|New York1.Background and Motivation2.Spoiler Alert:Key Takeaways3.Whats the Deal with Second-Order Uncertainty?4.Whats the Deal with Third-Order Uncertainty?5.Cat,Quota Share and PPR Case Studies:Ho

2、w Reliable are Our Percentiles?6.Practical Implications and Recommendations7.Appendices:Proof Outline and Further DetailsAgenda41.Background and Motivation5 5Background and MotivationTwo Questions1.What is the statistical nature of our simulated percentiles?What can I say about the error bands aroun

3、d the percentiles,and are the error bands themselves a bit stretchy/blurry with uncertainty?2.How many realizations should we run?The answer will depend on the context,but our decisions should be well-informed by empirical evidence and solid theory,including the answer to question 1Addressing these

4、two questions will help guide practical decisions,and ensure sound advice to brokers and clients6 6Background and MotivationTwo Questions1.What is the statistical nature of our simulated percentiles?What can I say about the error bands around the percentiles,and are the error bands themselves a bit

5、stretchy/blurry with uncertainty?2.How many realizations should we run?The answer will depend on the context,but our decisions should be well-informed by empirical evidence and solid theory,including the answer to question 1Addressing these two questions will help guide practical decisions,and ensur

6、e sound advice to brokers and clients7 7Background and MotivationTwo Questions1.What is the statistical nature of our simulated percentiles?What can I say about the error bands around the percentiles,and are the error bands themselves a bit stretchy/blurry with uncertainty?2.How many realizations sh

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本文主要探讨了模拟不确定性及其对尾部统计数据可靠性的影响。文章首先介绍了背景和动机,提出了两个关键问题:模拟百分位数统计性质以及需要运行多少次实现。接着,文章详细讨论了第二和第三级不确定性,即模拟百分位本身的不确定性和模拟百分位CV的不确定性。文章通过实证研究和理论分析,发现RMS猫模型尾部统计数据具有很高的不确定性,而配额份额和每风险XOL模型则更快收敛。最后,文章提出了实际应用中的建议,包括模拟足够多的实现以使尾部统计数据大致收敛,以及对于由RMS尾部统计数据驱动且未按区域/灾害进行分散的再保险决策,模拟超过200万次实现是有价值的。
如何可靠地估计尾部统计数据? 如何提高模拟的可靠性? 如何降低模拟结果的不确定性?
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