Modeling dependence structure among various risks, especially the measure of tail dependence and the aggregation of risks, is crucial for risk management. In this article, we present an extension to the traditional one-parameter Archimedean copulas by integrating the log-gamma-generated (LGG) margins. This class of novel multivariate distribution can better capture the tail dependence. The distortion effect on the classic one-parameter Archimedean copulas is well exhibited and the analytical expression of the sum of bivariate margins is proposed. The model provides a flexible way to capture tail risks and aggregate portfolio losses. Sufficient conditions for constructing a legitimate d-dimensional LGG Archimedean copula as well as the simulation framework are also proposed. Furthermore, two applications of this model are presented using concrete insurance datasets.

建模各种风险之间的依赖结构，特别是尾部依赖的度量和风险的聚合，对于风险管理至关重要。在本文中，我们通过集成对数伽马生成 (LGG) 边距，对传统的单参数阿基米德 copula 进行了扩展。这类新颖的多元分布可以更好地捕捉尾部依赖性。对经典的单参数阿基米德copulas的畸变效应得到了很好的体现，并提出了双变量边距和的解析表达式。该模型提供了一种灵活的方式来捕捉尾部风险和总投资组合损失。还提出了构建合法 d 维 LGG Archimedean copula 的充分条件以及模拟框架。此外，