论文标题
在随机损失保留中的数据不平衡数据和常见的冲击模型上
On unbalanced data and common shock models in stochastic loss reserving
论文作者
论文摘要
引入常见冲击是一种流行的依赖建模方法，并在保留损失方面有一些最新应用。这种方法的主要优点是能够捕获来自已知关系的结构依赖性。此外，它有助于较大维度的相关矩阵的简约构建。然而，在存在“不平衡数据”的情况下，就会出现并发症，即，当单个三角形上或三角形之间的观察值（预期）大小可能会大不相同。具体而言，如果对所有这些单元格应用了一个共同的冲击，则除非进行仔细的调整，否则它可能对较大的值和/或淹没较小的值无关紧要。涉及负索赔金额的应用程序更加复杂。在本文中，我们使用常见的减震Tweedie方法在损失保留上下文中解决了这个问题。我们表明，该解决方案不仅提供了相对于不平衡数据的共同冲击比例的更好平衡，而且还可以放松。最后，常见的冲击Tweedie模型还提供了分布障碍。
Introducing common shocks is a popular dependence modelling approach, with some recent applications in loss reserving. The main advantage of this approach is the ability to capture structural dependence coming from known relationships. In addition, it helps with the parsimonious construction of correlation matrices of large dimensions. However, complications arise in the presence of "unbalanced data", that is, when (expected) magnitude of observations over a single triangle, or between triangles, can vary substantially. Specifically, if a single common shock is applied to all of these cells, it can contribute insignificantly to the larger values and/or swamp the smaller ones, unless careful adjustments are made. This problem is further complicated in applications involving negative claim amounts. In this paper, we address this problem in the loss reserving context using a common shock Tweedie approach for unbalanced data. We show that the solution not only provides a much better balance of the common shock proportions relative to the unbalanced data, but it is also parsimonious. Finally, the common shock Tweedie model also provides distributional tractability.
