Dependence Modeling in Insurance and Finance: Estimation, Ratemaking and Reserving

保险和金融中的依赖模型：估计、费率制定和准备金

• 批准号: RGPIN-2021-04144
• 负责人: Lu, Yang
• 金额: $ 1.89万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759048
• 关键词: Dependence; Modeling; Insurance; Finance; Estimation

This program focuses on dependence modelling between highly non-continuous variables encountered in Insurance and credit risk, such as binary, count or positive variables (with potentially censoring, or point mass at zero), or bounded variables. Such data can be available either crosssectionally, or longitudinally, and are typically observed with high--dimensional covariates. For instance, in non-life insurance, one typically observes, for each individual, a claim count and a positive variable (total claim severity); in credit risk, one observes a binary indicator (the default indicator), as well as the loss--given-default (LGD) in case of default, which is bounded by 0 and 1, with possibly probability masses at bounds. In both applications, the dependence between the response variables is essential for risk management but their modeling is challenging due to the non-continuous feature. Despite the similarities of the data features, the two communities remain somehow disconnected, and face similar challenges. In particular, although recently both domains have embraced machine learning (ML) techniques, many of them have also their own downsides. My long-term objective is to i) bring the two fields closer, through the introduction of new methodologies useful in both fields, as well as appropriate technology transfer, and ii) bridge the link between traditional statistical models and machine learning (ML) techniques for dependence modelling. More precisely, my detailed objectives are as follows: -Extend the applicability of random effect models, which is a powerful family of (statistical) models for dependence modeling in insurance and credit risk, to new settings and new applications.  -Reconcile ML methods with the characteristics of financial data, and mitigate the shortcomings of existing ML methods currently proposed in the actuarial and credit risk literature. In particular I will consider ML methods that are suitable for multivariate and/or longitudinal data, so that dependence can be effectively taken into account. -Confront traditional approaches and ML methods, both empirically, and theoretically. In particular I will try to answer a long-term debate on whether and how dependence should be accounted for between credit default and the associated loss-given default, and look for new, economically-relevant model selection criteria that account for potential dependence. The expected impacts of this program is i) through training of qualified graduate students and collaboration, bridging the gap between academia and industry, ; ii) through a unified research program, bringing closer the actuarial and credit risk domains, both in industry and academia. iii) through careful analysis and comparison of both traditional and ML methods, contributing to a better understanding of the true power of ML methods for retail financial/insurance.

该程序侧重于在保险和信用风险中遇到的高度非连续变量之间的相关性建模，例如二进制，计数或正变量（具有潜在的删失，或点质量为零），或有界变量。这些数据可以在横截面或纵向上提供，并且通常使用高维协变量进行观察。例如，在非人寿保险中，人们通常会观察到每个人的索赔数量和一个正变量（总索赔严重程度）;在信用风险中，人们会观察到一个二元指标（违约指标），以及违约情况下的违约损失（LGD），其界限为0和1，可能有概率质量。在这两种应用中，响应变量之间的相关性对于风险管理是必不可少的，但由于非连续性，它们的建模具有挑战性。尽管数据特征相似，但这两个社区仍然在某种程度上脱节，并面临类似的挑战。特别是，尽管最近这两个领域都采用了机器学习（ML）技术，但其中许多也有自己的缺点。我的长期目标是：i）通过引入在这两个领域都有用的新方法以及适当的技术转让，使这两个领域更加紧密; ii）在传统统计模型和机器学习（ML）技术之间建立联系，以进行依赖建模。更确切地说，我的详细目标如下：-将随机效应模型的适用性扩展到新的环境和新的应用中，随机效应模型是一个强大的（统计）模型家族，用于保险和信用风险的依赖建模。-将ML方法与金融数据的特征相结合，并减轻目前在精算和信用风险文献中提出的现有ML方法的缺点。特别是，我将考虑适用于多变量和/或纵向数据的ML方法，以便有效地考虑相关性。- 从经验和理论上对抗传统方法和ML方法。特别是，我将试图回答一个长期的争论，是否以及如何依赖应占之间的信用违约和相关的损失给定的违约，并寻找新的，经济相关的模型选择标准，占潜在的依赖。该计划的预期影响是：i）通过培训合格的研究生和合作，弥合学术界和工业界之间的差距; ii）通过统一的研究计划，拉近精算和信用风险领域，无论是在工业界还是学术界。iii）通过对传统方法和ML方法的仔细分析和比较，有助于更好地理解ML方法在零售金融/保险领域的真正力量。