EAGER: III: CIFRAM: Distributed Computing Approaches for the Analysis of Enterprise and Systemic Risk using a Financial Contract-Based Infrastructure

EAGER：III：CIFRAM：使用基于金融合同的基础设施分析企业和系统风险的分布式计算方法

• 批准号: 1445403
• 负责人: Donald Berndt
• 金额: $ 29.87万
• 依托单位: University of South Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1445403&HistoricalAwards=false
• 关键词: EAGER; III; CIFRAM; Distributed; Computing; EAGER; III; CIFRAM; Distributed; Computing

Following the recent financial crisis, regulatory responses and academic initiatives spurred significant research into the measurement of systemic risk. Most systemic risk models are bound however by the constraints of available data and computing platforms. Many researchers argue that the possibilities for understanding systemic risk will be greatly enhanced if the relationships and contractual arrangements between markets participants can be modeled at a granular level. The objective of this research is to accelerate our understanding of systemic risk monitoring approaches that leverage granular data (transactional, positional and other) by establishing a model for (and implementation of) the underlying distributed computing platform.This research focuses on the implementation of a reference architecture and technical platform designed to cater to the specific data fabric of the financial system and addressing the computational requirements associated with the analysis of systemic risk using granular contract-level data. Research efforts are focused around the following two objectives: (1) Evaluation and implementation of granular contract-level data models using scalable database technologies. This will entail the analysis of alternative design patterns and data models, the evaluation of database technologies (including the relational model but with specific focus on the NoSQL universe) and the implementation of data models using the best fit technology; (2) Prototyping and evaluation of distributed computing approaches based on the underlying database foundation. This will entail using MapReduce or other parallel computing frameworks to harness the power of many processing resources. The ACTUS (Algorithmic Contract Types Unified Standard) calculation engine will be integrated with the computing environment in order to provide granular event vectors and state-contingent cash flows. Building on the granular cash flow information, this research will prototype, test and evaluate various aggregation frameworks for the calculation of summary analytics.For further information see the project web site: http://www.usf.edu/business/departments/isds/projects/gsrisk

在最近的金融危机之后，监管反应和学术举措促使对系统风险的衡量进行了重大研究。大多数系统性风险模型都受到可用数据和计算平台的限制的约束。 许多研究人员认为，如果可以在颗粒状的水平上建模市场之间的关系和合同安排，那么理解系统风险的可能性将大大增强。 这项研究的目的是通过建立（和实施）基础分布式计算平台的模型来加速我们对系统性风险监控方法的理解，这些方法利用颗粒数据（交易，位置和其他）。这项研究重点介绍了旨在符合财务系统和计算数据分析的特定数据结构的参考架构和技术平台的实施。 研究工作集中在以下两个目标上：（1）使用可扩展的数据库技术评估和实施颗粒状合同级数据模型。 这将需要分析替代设计模式和数据模型，数据库技术的评估（包括关系模型，但针对NOSQL宇宙），并使用最佳拟合技术对数据模型进行实施； （2）基于基础数据库基础的分布式计算方法的原型和评估。 这将需要使用MapReduce或其他并行计算框架来利用许多处理资源的功能。 ACTU（算法合同类型统一标准）计算引擎将与计算环境集成，以提供颗粒状事件向量和国家固定现金流量。 Building on the granular cash flow information, this research will prototype, test and evaluate various aggregation frameworks for the calculation of summary analytics.For further information see the project web site: http://www.usf.edu/business/departments/isds/projects/gsrisk