英文标题：
《A Novel Approach to Quantification of Model Risk for Practitioners》
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作者：
Zuzana Krajcovicova, Pedro Pablo Perez-Velasco and Carlos Vazquez
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最新提交年份：
2017
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英文摘要：
Models continue to increase their already broad use across industry as well as their sophistication. Worldwide regulation oblige financial institutions to manage and address model risk with the same severity as any other type of risk, which besides defines model risk as the potential for adverse consequences from decisions based on incorrect and misused model outputs and reports. Model risk quantification is essential not only in meeting these requirements but for institution\'s basic internal operative. It is however a complex task as any comprehensive quantification methodology should at least consider the data used for building the model, its mathematical foundations, the IT infrastructure, overall performance and (most importantly) usage. Besides, the current amount of models and different mathematical modelling techniques is overwhelming. Our proposal is to define quantification of model risk as a calculation of the norm of some appropriate function that belongs to a Banach space, defined over a weighted Riemannian manifold endowed with the Fisher--Rao metric. The aim of the present contribution is twofold: Introduce a sufficiently general and sound mathematical framework to cover the aforementioned points and illustrate how a practitioner may identify the relevant abstract concepts and put them to work.
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中文摘要：
模型继续增加其在整个行业中的广泛使用及其复杂性。全球监管要求金融机构管理和处理与任何其他类型风险同等严重的模型风险，此外，这还将模型风险定义为基于错误和误用模型输出和报告的决策可能产生不利后果。模型风险量化不仅对满足这些要求至关重要，而且对机构的基本内部运作也至关重要。然而，这是一项复杂的任务，因为任何全面的量化方法都应至少考虑用于构建模型的数据、其数学基储It基础设施、总体性能和（最重要的）使用情况。此外，目前的模型数量和不同的数学建模技术是压倒性的。我们的建议是将模型风险的量化定义为计算某个适当函数的范数，该函数属于Banach空间，定义在赋予Fisher-Rao度量的加权黎曼流形上。本文的目的有两个：介绍一个足够普遍和完善的数学框架，以涵盖上述各点，并说明从业者如何识别相关的抽象概念并将其付诸实践。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Risk Management 风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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