标题： 贝叶斯偏重尾模型在准备金评估中的应用 显示英文标题

标题： A Bayesian Skew-heavy-tailed modelling for loss reserving

摘要： 本文专注于对未付索赔进行损失准备建模。 由于任何给定索赔的金额在结算前未知，我们提出了一种通过重尾和偏斜分布的灵活模型来处理未付负债。 推断依赖于通过吉布斯采样器的马尔可夫链蒙特卡罗方法，其中包含自适应梅特ropolis算法步骤，允许快速计算并提供高效的算法。 一个示例模拟了一个基于溢出三角形的典型数据集，并研究了所提出的模型的性质。 此外，还考虑了一个案例研究，并表明在存在偏度和重尾的情况下，所提出的模型在性能上优于文献中广泛建立的常规损失准备模型。 显示英文摘要

摘要： This paper focuses on modelling loss reserving to pay outstanding claims. As the amount liable on any given claim is not known until settlement, we propose a flexible model via heavy-tailed and skewed distributions to deal with outstanding liabilities. The inference relies on Markov chain Monte Carlo via Gibbs sampler with adaptive Metropolis algorithm steps allowing for fast computations and providing efficient algorithms. An illustrative example emulates a typical dataset based on a runoff triangle and investigates the properties of the proposed models. Also, a case study is considered and shows that the proposed model outperforms the usual loss reserving models well established in the literature in the presence of skewness and heavy tails.