Modelling of non-bank financial institutions

非银行金融机构建模

• 批准号: 2441936
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2441936
• 关键词: Modelling; non; bank; financial; institutions

The development of mathematical models for the analysis of financial stability and systemic risk in the financial system has been the focus of a lot of research efforts in the aftermath of the 2007-2008 financial crisis. Much of this research has been focused on the analysis of the stability of the banking system and the regulation of banks. However, the banking system is only a subset of the financial system, which also includes many types of Onon-bankO financial institutions, most notable asset managers such as mutual funds, pension funds, money market funds, hedge funds, as well as insurance companies. These Onon-banksO hold a sizable fraction of financial assets and account for a large proportion of transactions, so an accurate understanding of their role and their interactions with the banking system is crucial for a realistic modelling of the financial system.This project aims at improving our understanding of the role of non-bank financial institutions by exploiting regulatory filings and market data to develop a detailed description of the ecology of the financial system. The first step is a statistical analysis of regulatory data and market data in which we will make use of classification methods and modern methods of data analysis in order to arrive at a stable and interpretable partition of financial market participations into groups following similar strategies and to characterize the financial environment at micro and macro levels. Our aim is to characterize the main categories of non-banks in the financial system and identify their main features and interactions.We take advantage of machine learning techniques to characterize the behavior of non-banks by exploiting all the temporal and spatial information that we can extract from holdings and market data.A second step will be to model the behavior of these non-banks in a mathematical framework which lends itself to analytical study or simulations which may be useful for stress testing and risk analysis, and for examining the consequences for systemic risk and financial stability. This project falls within the following EPSRC research areas: artificial intelligence technologies, statistics and applied probability.Fidelity Investments Inc. is the industrial partner for this project.

在2007-2008年金融危机之后，为分析金融系统的稳定性和系统性风险而开发的数学模型成为了许多研究工作的焦点。这方面的研究主要集中在分析银行体系的稳定性和银行监管。然而，银行系统只是金融系统的一个子集，金融系统还包括许多类型的Onon-bankO金融机构，最著名的资产管理公司，如共同基金，养老基金，货币市场基金，对冲基金以及保险公司。这些非银行持有相当大比例的金融资产，并占交易的很大比例，因此，准确理解它们的作用以及它们与银行体系的相互作用，对于金融体系的现实建模至关重要。银行金融机构通过利用监管文件和市场数据来制定金融体系生态的详细描述。第一步是对监管数据和市场数据进行统计分析，我们将利用分类方法和现代数据分析方法，以便将金融市场参与者按照类似的战略进行稳定和可解释的划分，并在微观和宏观层面描述金融环境的特征。我们的目标是描述金融系统中非银行机构的主要类别，并确定它们的主要特征和相互作用。我们利用机器学习技术，通过利用我们可以从持有和市场数据中提取的所有时间和空间信息来描述非银行机构的行为。第二步是对这些非银行机构的行为进行建模，这是一个非常重要的步骤。银行在一个数学框架，适合自己的分析研究或模拟，这可能是有用的压力测试和风险分析，并为审查的后果，系统性风险和金融稳定。该项目福尔斯以下EPSRC研究领域：人工智能技术，统计学和应用概率。是这个项目的工业合作伙伴。