基于Copula的动态和粒度损失预留
Dynamic and granular loss reserving with copulae
摘要：
近年来，保险中的随机方法得到了广泛的研究。为应付未来所有因保单而产生的索偿，必须量化未偿损失负债。损失准备金的计算方法主要是基于径流三角形的汇总数据。传统的储备技术有一些缺点：由于三角形中的聚集、零或负单元格，来自保单和索赔的发展的信息丢失;三角形中的观察值通常很少;最近事故年份的观察值很少;以及对最近支付的索赔的敏感性。为了克服这些困境，粒度损失准备金方法的个人索赔索赔数据将得出。储量估算是风险评估过程的重要组成部分，也是经济学研究的前沿课题。由于对不同类型的索赔甚至多个业务线的总准备金预测的需求越来越大，因此将建立一个时变Copula框架用于粒度准备金。
英文摘要：
An intensive research sprang up for stochastic methods in insurance during the past years. To meet all future claims rising from policies, it is requisite to quantify the outstanding loss liabilities. Loss reserving methods based on aggregated data from run-off triangles are predominantly used to calculate the claims reserves. Conventional reserving techniques have some disadvantages: loss of information from the policy and the claim's development due to the aggregation, zero or negative cells in the triangle; usually small number of observations in the triangle; only few observations for recent accident years; and sensitivity to the most recent paid claims. To overcome these dilemmas, granular loss reserving methods for individual claim-by-claim data will be derived. Reserves' estimation is a crucial part of the risk valuation process, which is now a front burner in economics. Since there is a growing demand for prediction of total reserves for different types of claims or even multiple lines of business, a time-varying copula framework for granular reserving will be established.
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。
