保险损失准备金的集成分布预测
Ensemble distributional forecasting for insurance loss reserving
AI总结：
本文提出一个系统性框架以客观集成多个随机损失准备金模型，通过考虑完整分布特性和准备金数据固有特征进行优化组合，在合成数据集中表现优于传统模型选择与等权集成方法。
摘要：
损失准备金通常侧重于识别能够产生卓越预测性能的单一模型。然而，不同的损失准备金模型专门用于捕捉损失数据的不同方面。实践中这一点已得到认可，即人们经常会考虑不同模型的结果，有时甚至将其结合。例如，精算师可能会基于主观评估，对各种损失准备金模型的预测结果取加权平均值。\n在本文中，我们提出了一个系统性框架，以客观地组合（即集成）多个随机损失准备金模型，从而有效利用不同模型提供的优势。与现有文献和实践相比，我们的框架有两大主要创新。首先，我们进行模型组合的标准考虑了集成的完整分布特性，而不仅仅是中心估计值——这在准备金语境中尤为重要。其次，我们的框架针对准备金数据固有的特征进行了量身定制。例如，这些特征包括事故期、发展期、日历期和索赔成熟度效应。关键在于，不同事故期之间数据的相对重要性和稀缺性，使得该问题有别于统计学习中的传统集成技术。\n我们的框架通过一个复杂的合成数据集进行了说明。结果显示，优化后的集成模型优于传统的模型选择方法和等权集成方法。具体而言，这种改进不仅体现在中心估计值上，还体现在相关的分位数上，例如第95分位数的准备金（这通常是保险公司和监管机构关注的重点）。本文开发的框架可通过R包ADLP实现，该包可从CRAN获取。
英文摘要：
Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss reserving models specialise in capturing different aspects of loss data. This is recognised in practice in the sense that results from different models are often considered, and sometimes combined. For instance, actuaries may take a weighted average of the prediction outcomes from various loss reserving models, often based on subjective assessments. In this paper, we propose a systematic framework to objectively combine (i.e. ensemble) multiple _stochastic_ loss reserving models such that the strengths offered by different models can be utilised effectively. Our framework contains two main innovations compared to existing literature and practice. Firstly, our criteria model combination considers the full distributional properties of the ensemble and not just the central estimate - which is of particular importance in the reserving context. Secondly, our framework is that it is tailored for the features inherent to reserving data. These include, for instance, accident, development, calendar, and claim maturity effects. Crucially, the relative importance and scarcity of data across accident periods renders the problem distinct from the traditional ensembling techniques in statistical learning. Our framework is illustrated with a complex synthetic dataset. In the results, the optimised ensemble outperforms both (i) traditional model selection strategies, and (ii) an equally weighted ensemble. In particular, the improvement occurs not only with central estimates but also relevant quantiles, such as the 75th percentile of reserves (typically of interest to both insurers and regulators). The framework developed in this paper can be implemented thanks to an R package, `ADLP`, which is available from CRAN.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
