英文标题：
《Copula based hierarchical risk aggregation - Tree dependent sampling and
  the space of mild tree dependence》
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作者：
Fabio Derendinger
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最新提交年份：
2015
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英文摘要：
  The ability to adequately model risks is crucial for insurance companies. The method of \"Copula-based hierarchical risk aggregation\" by Arbenz et al. offers a flexible way in doing so and has attracted much attention recently. We briefly introduce the aggregation tree model as well as the sampling algorithm proposed by they authors.   An important characteristic of the model is that the joint distribution of all risk is not fully specified unless an additional assumption (known as \"conditional independence assumption\") is added. We show that there is numerical evidence that the sampling algorithm yields an approximation of the distribution uniquely specified by the conditional independence assumption. We propose a modified algorithm and provide a proof that under certain conditions the said distribution is indeed approximated by our algorithm.   We further determine the space of feasible distributions for a given aggregation tree model in case we drop the conditional independence assumption. We study the impact of the input parameters and the tree structure, which allows conclusions of the way the aggregation tree should be designed.
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中文摘要：
充分建模风险的能力对保险公司至关重要。Arbenz等人提出的“基于Copula的层次风险聚合”方法提供了一种灵活的方法，最近引起了广泛关注。我们简要介绍了聚合树模型以及作者提出的采样算法。该模型的一个重要特征是，除非添加额外的假设（称为“条件独立假设”），否则所有风险的联合分布并没有得到充分规定。我们证明，有数值证据表明，抽样算法可以得到由条件独立性假设唯一指定的分布的近似值。我们提出了一种改进的算法，并证明在某些条件下，我们的算法确实近似于所述分布。在放弃条件独立假设的情况下，我们进一步确定了给定聚合树模型的可行分布空间。我们研究了输入参数和树结构的影响，从而得出聚合树的设计方法。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Risk Management        风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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