论文标题
检测和处理异常值多变量可靠损失保留
Detection and treatment of outliers for multivariate robust loss reserving
论文作者
论文摘要
众所周知，用于计算未偿还链路梯子等未偿债权负债的传统技术有可能在过去的索赔数据中被异常值扭曲的风险。不幸的是，在强大的保留方法中，文献很少，但诸如Verdonck和Debruyne（2011）以及Verdonck和Verdonck和Van Wouwe（2011）之类的例外。在本文中，我们提出了两种替代性强大的双变量链条技术，以扩展Verdonck和van Wouwe的方法（2011年）。第一种技术是基于调整后的外表性（Hubert and van der Veeken，2008），并明确地将偏斜度纳入分析中，同时为每个观测值提供了独特的远方量度。第二种技术是基于Bagdistance（Hubert等，2016），该技术源自Bagplot，它能够提供独特的外向图衡量标准，以及一种基于此措施来调整外围观察结果的手段。 此外，我们将强大的双变量链路方法扩展到N维框架。这些方法的实施，尤其是双变量之外的方法，并不是微不足道的。这在澳大利亚一般保险公司的三横向数据集上进行了说明，并比较了不同的异常检测和治疗机制的结果。
Traditional techniques for calculating outstanding claim liabilities such as the chain ladder are notoriously at risk of being distorted by outliers in past claims data. Unfortunately, the literature in robust methods of reserving is scant, with notable exceptions such as Verdonck and Debruyne (2011) and Verdonck and Van Wouwe (2011). In this paper, we put forward two alternative robust bivariate chain-ladder techniques to extend the approach of Verdonck and Van Wouwe (2011). The first technique is based on Adjusted Outlyingness (Hubert and Van der Veeken, 2008) and explicitly incorporates skewness into the analysis whilst providing a unique measure of outlyingness for each observation. The second technique is based on bagdistance (Hubert et al., 2016) which is derived from the bagplot however is able to provide a unique measure of outlyingness and a means to adjust outlying observations based on this measure. Furthermore, we extend our robust bivariate chain-ladder approach to an N-dimensional framework. The implementation of the methods, especially beyond bivariate, is not trivial. This is illustrated on a trivariate data set from Australian general insurers, and results under the different outlier detection and treatment mechanisms are compared.
