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

University Of South Florida, Tampa FL

Investigators

Abstract

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