Sensitivity, uncertainty and robustness of risk models in insurance

保险风险模型的敏感性、不确定性和稳健性

• 批准号: RGPIN-2020-04289
• 负责人: Pesenti, Silvana
• 金额: $ 1.31万
• 依托单位: University of Toronto
• 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=765688
• 关键词: Sensitivity; uncertainty; robustness; risk; models

Insurance companies are exposed to a unique landscape of risks including financial and insurance risks. Insurance contracts have extremely long maturities, resulting in long-dated risks, and are typically grouped into extremely heterogeneous portfolios. Understanding the risks embedded in the aggregation of such heterogeneous portfolios is highly non-trivial, particularly due to challenges in estimating and specifying interdependence. Numerical and computational advances, notably in machine learning techniques, accelerated the use of quantitative, highly sophisticated statistical models throughout the insurance industry. Typically in financial and insurance risk management, far-reaching decisions are grounded on low dimensional summaries of model outputs. Thus, understanding output variability resulting from uncertainty in model components is critical for making well-informed decisions. Risk managers and regulation alike call for quantification of model stability, robustness, and sensitivity. One of this research proposal's vision is to develop new and original sensitivity und uncertainty analysis tools for models used in financial and insurance risk management. Thus, addressing the immense demand from risk managers, policy makers, and model (end-)users for reliable tools to understand and gain insight into their models' uncertainties and limitations. Misspecification in model components are ubiquitous and arise from parameter estimation, from missing data, and from inaccurate collection of data. Analyzing the propagation of misspecification, that is the cascading of stresses from one single model component through an entire model to the model's output, provides invaluable information about the model's structure. Specifically, dependences between model components are the critical drivers of propagation of stresses and form the core of this proposal's second research stream. Understanding the cascading effects of stresses have applications in financial risk management, such as systemic risk and stress testing. As performing stress tests is a regulatory requirement for banking institutions and insurance companies alike, and that systemic stability of the financial system is of paramount concern to policy makers, this research proposal has high potential for industrial applications and practical and regulatory relevance.

保险公司面临着独特的风险，包括金融和保险风险。保险合同的到期日极长，导致长期风险，通常被分组为极其异质的投资组合。理解这种异质投资组合的聚合中蕴含的风险是非常重要的，特别是由于估计和指定相互依赖性方面的挑战。数字和计算的进步，特别是机器学习技术的进步，加速了整个保险业对定量、高度复杂的统计模型的使用。通常在金融和保险风险管理中，影响深远的决策基于模型输出的低维摘要。因此，了解模型组件中的不确定性导致的输出可变性对于做出明智的决策至关重要。风险管理者和监管机构都要求量化模型的稳定性、鲁棒性和敏感性。本研究计划的愿景之一是为金融和保险风险管理中使用的模型开发新的和原始的敏感性和不确定性分析工具。因此，解决了风险管理者、政策制定者和模型（最终）用户对可靠工具的巨大需求，以了解和洞察其模型的不确定性和局限性。模型组件中的错误指定是普遍存在的，并且由参数估计、缺失数据和不准确的数据收集引起。分析错误指定的传播，即从单个模型组件通过整个模型到模型输出的应力级联，可以提供有关模型结构的宝贵信息。具体而言，模型组件之间的依赖性是应力传播的关键驱动因素，并形成了该提案的第二个研究流的核心。理解压力的级联效应在金融风险管理中有应用，例如系统性风险和压力测试。由于进行压力测试是对银行机构和保险公司的监管要求，而金融体系的系统稳定性是政策制定者最关心的问题，因此本研究建议具有很高的工业应用潜力以及实际和监管相关性。