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

气候和天气灾害对全球年度GDP的综合威胁超过1300亿美元（剑桥风险研究中心，2020年），使其成为任何可想象的GDP威胁中最大的，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），巨灾（cat）模型作为一种风险管理工具已成为（再）保险公司不可或缺的工具，并开始迎来一个提高对风险理解的新时代。猫模特起源于20世纪80年代末，在过去的30年里不断发展，以帮助解决全球社会长期可持续性面临的一些最复杂的挑战。在他关于气候变化和金融稳定的演讲中，当时的英格兰银行行长直接赞扬了这些模型，他说：“英国保险公司经受住了2011年的事件，这是有史以来保险损失最严重的一年之一。”你的模型得到了验证，索赔得到了支付，偿付能力得到了维持”（Mark Carney, 2015）。虽然猫模特的出现无疑给社会带来了许多好处，但我们还远远没有从私人行业和社会中提取出最大的价值。我们仍然有能力对灾难性事件感到震惊，这些事件不仅很容易想象，而且在当代猫模型中得到了很好的体现，这说明这项服务尚未充分发挥其潜力。与许多长期存在的学术学科相比，猫建模领域仍处于萌芽阶段，这些学科的数据和信息为其过程提供了依据。这也许可以解释为什么大部分潜在价值目前仍未开发。不幸的是，在过去的三十年里，猫建模行业主要专注于部署针对狭义风险定义的分析，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- LOSS-SAMPLER -它利用现代猫模型平台来扩大这些狭窄的风险定义。这使我们能够开始发现“未知的未知”——存在于猫模型分析创建中的信息，但由于与应用程序相关的操作复杂性，这些信息一直被忽视。这个分析引擎将被改进以解决过去几年需求迅速增长的三个特定业务挑战，即：1。协助评估cat模型并实施可靠的风险视图2。通知猫风险投资组合管理。应对这些挑战将有助于在全球金融体系中建立应对气候变化的韧性。

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.