BECS: Rare Systematic Risk in Markets: Modelling, Theory and Computation

BECS：市场中罕见的系统性风险：建模、理论和计算

• 批准号: 1024837
• 负责人: Richard Sowers
• 金额: $ 31万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1024837&HistoricalAwards=false
• 关键词: BECS; Rare; Systematic; Risk; Markets

This is an exploratory proposal which seeks to understand the interaction between complexity and rare events in financial systems. Specifically, we seek to model the behavior of central counterparties and banks. Rare events in such systems often come from interaction between various parts of the system. We will use the tools of large deviations and noncooperative game theory to characterize various aspects of how systemic and idiosyncratic risk propagate through nonlinearities in high-dimensional financial systems.The focus of this proposal is on two problems which highlight several aspects of complexity in several exemplary financial systems. In particular, we are interested in central counterparties and banks. The complexity which we wish to investigate is the variety of risks which can affect financial systems, and the (nonlinear) feedbacks between them. Our motivation in these problems is to understand and control pathways of financial collapse. Assumedly, regulatory requirements make financial collapse rare. Amongst these rare configurations corresponding to financial meltdown or market collapse, which ones are the ``most'' likely? How can we efficiently simulate these scenarios? Furthermore, can we control the system and design suitable market mechanisms so that meltdown, if it occurs, is most likely to occur in some ``preferred'' way? An intrinsic part of this analysis is the inherently noncooperative nature of financial systems; they involve a large number of agents, each of whom seeks to maximize its own profit. When considering the associated control problem, we observe that the large population of agents leads to high dimensional problems that may often be intractable. We intend to examine whether mean-field approximations may be employed to obtain a characterization of aggregate behavior. Additionally, Our focus is the impact of this structure on rare events. The competing interactions between the different parts of the system imply that the behavior of the system cannot in general be fully understood by looking solely at a part of the system.

这是一项探索性提案，旨在了解金融系统中的复杂性和罕见事件之间的相互作用。 具体来说，我们寻求对中央对手方和银行的行为进行建模。 此类系统中的罕见事件通常来自系统各个部分之间的交互。 我们将使用大偏差和非合作博弈论的工具来描述系统性风险和特殊风险如何通过高维金融系统中的非线性传播的各个方面。本提案的重点是两个问题，它们突出了几个示例性金融系统中复杂性的几个方面。 我们特别对中央对手方和银行感兴趣。 我们希望研究的复杂性是影响金融系统的各种风险，以及它们之间的（非线性）反馈。 我们解决这些问题的动机是了解和控制金融崩溃的路径。 据推测，监管要求使得金融崩溃的情况很少发生。 在这些与金融崩溃或市场崩溃相对应的罕见配置中，哪些是“最”有可能的？ 如何有效地模拟这些场景？ 此外，我们能否控制系统并设计合适的市场机制，以便崩溃（如果发生）最有可能以某种“首选”方式发生？ 这一分析的一个内在部分是金融体系固有的非合作性质；它们涉及大量代理商，每个代理商都寻求自身利润最大化。 在考虑相关的控制问题时，我们观察到大量的智能体会导致高维问题，而这些问题通常可能很棘手。我们打算研究是否可以采用平均场近似来获得聚合行为的表征。此外，我们的重点是这种结构对罕见事件的影响。 系统不同部分之间的竞争性相互作用意味着系统的行为通常不能仅通过观察系统的一部分来完全理解。