LOSS-SAMPLER: Tools for quantifying and managing unseen volatility in CER portfolios

LOSS-SAMPLER：用于量化和管理 CER 投资组合中未见波动的工具

• 批准号: 10057920
• 金额: $ 41.92万
• 依托单位: MAXIMUM INFORMATION CONSULTING LTD
• 依托单位国家: 英国
• 项目类别: Small Business Research Initiative
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10057920
• 关键词: LOSS; SAMPLER; Tools; quantifying; managing

The combined threat of climate and weather perils to annual global GDP is over $130billion (Cambridge Centre for Risk Studies, 2020), making them the largest of any conceivable threat to GDP, whether man-made or natural in origin. In the face of such severe Climate and Environmental Risk (CER), catastrophe (cat)-modelling has become indispensable to (re)insurers as a risk-management tool, and has begun to usher in a new era of improved understanding of risk.Originating in the late 1980s, cat-modelling has evolved over the past ~30years to help tackle some of the most complex challenges facing the long-term sustainability of global society. In his speech on Climate Change and Financial Stability, the then Governor of the Bank of England directly praised the models, stating that "UK insurers withstood the events of 2011, one of the worst years on record for insurance losses. Your models were validated, claims were paid, and solvency was maintained" (Mark Carney, 2015).While there have undoubtedly been many benefits to society from the advent of cat-modelling, we are far from extracting its maximum value to private industry and society. Our capacity to still be shocked by catastrophic events that are not just easily conceivable, but well-represented in contemporary cat-models, speaks of a service that has yet to realise its full potential.The field of cat-modelling is still nascent compared to the many longstanding academic disciplines whose data and information feed its processes. This may explain why much of the potential value currently remains untapped. Somewhat unfortunately, over the past three decades the cat-modelling industry has primarily focused on deploying analytics that target a narrow definition of risk - that is, a definition that is easily operationalised in risk pricing and capital management situations.We have created an analytical engine - LOSS-SAMPLER - that utilises contemporary cat-model platforms to broaden these narrow definitions of risk. This allows us to begin uncovering "unknown-knowns" - information that exists in the creation of cat-model analytics but has historically been overlooked because of operational complexities associated with its application.This analytical engine will be evolved to tackle three specific business challenges for which demand has rapidly increased in the past few years, namely:1. assisting in the evaluation of cat-models and implementing reliable views of risk2. informing cat-risk portfolio management3. quantifying and communicating the impact of climate changeAddressing these challenges will facilitate the building of CER resilience in the global financial system.

年度全球GDP的气候和天气危险的综合威胁超过1300亿美元（剑桥风险研究中心，2020年），使其成为对GDP的任何可能威胁中的最大威胁，无论是人为的还是自然的。面对如此严重的气候和环境风险（CER），灾难（CAT）模型已成为（重新）保险作为一种风险管理工具，并且已经开始吸引人们对风险的改善的新时代。在1980年代后期，人们在过去的30年中遇到了巨大的挑战。当时英格兰银行的州长在他关于气候变化和财务稳定性的讲话中直接称赞了这些模式，并指出：“英国保险公司经受了2011年的持续事件，这是保险损失记录的最糟糕的几年之一。您的模型得到了验证，支付了索赔，并支付了偿付能力，并维持了许多人的最大利益（Mark Carney，2015年）。工业与社会。我们仍然被灾难性事件感到震惊的能力不仅可以容易想象，而且在当代猫模型中代表性良好，谈到了一项尚未意识到其全部潜力的服务。与许多长期以来的学术学科相比，Cat-Modelling领域仍然偏生，其数据和信息供应其流程。这可以解释为什么目前尚未开发的许多潜在价值。不幸的是，在过去的三十年中，猫建模行业主要集中于部署针对风险狭窄定义的分析（即，在风险定价和资本管理情况下易于操作的定义，我们创建了一个分析引擎 - 损失 - 采样器 - 利用当代猫模式平台来扩大这些狭窄的风险定义。这使我们能够开始发现“未知数” - 在创建Cat-Model Analytics中存在的“未知数”信息，但由于与其应用相关的操作复杂性，历史上一直被忽略。这将进化为解决三个特定的商业挑战，这些特定业务挑战在过去的几年中迅速增加了，即：1：1。协助评估猫模型并实施可靠的风险观点2。通知猫风险投资组合管理3。量化和交流气候变化的影响，这些挑战将有助于在全球金融体系中建立弹性。