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

该程序侧重于在保险和信用风险中遇到的高度不连续变量之间的依赖建模，例如二进制，计数或正变量（具有潜在的审查，或点质量为零）或有界变量。这些数据既可以是横断面的，也可以是纵向的，并且通常是用高维协变量来观察的。例如，在非寿险中，人们通常会观察到，对于每个人，索赔数量和一个正变量（索赔总严重性）；在信用风险中，人们观察到一个二元指标（违约指标），以及违约情况下的给定违约损失（LGD），其边界为0和1，边界上可能有概率质量。在这两种应用中，响应变量之间的依赖关系对风险管理至关重要，但由于其非连续性特征，它们的建模具有挑战性。尽管数据特征相似，但这两个社区在某种程度上仍然是脱节的，并面临着类似的挑战。特别是，尽管最近这两个领域都采用了机器学习（ML）技术，但其中许多技术也有自己的缺点。我的长期目标是i)通过引入对这两个领域都有用的新方法，以及适当的技术转让，拉近这两个领域的距离，以及ii)在传统统计模型和机器学习（ML）技术之间建立联系，用于依赖性建模。更准确地说，我的详细目标如下：-扩展随机效应模型的适用性，它是一个强大的（统计）模型家族，用于保险和信用风险的依赖性建模，到新的设置和新的应用。-调和机器学习方法与金融数据的特征，并减轻目前在精算和信用风险文献中提出的现有机器学习方法的缺点。特别是，我将考虑适合多变量和/或纵向数据的ML方法，以便可以有效地考虑依赖性。-面对传统方法和机器学习方法，无论是经验上还是理论上。特别是，我将试图回答关于信用违约和相关损失违约之间是否以及如何考虑依赖关系的长期争论，并寻找新的、经济相关的模型选择标准来考虑潜在的依赖关系。该计划的预期影响是：1)通过培养合格的研究生和合作，弥合学术界和工业界之间的差距；Ii)通过一个统一的研究项目，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。

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.