Thank you for your interest in our NSF grant titled: EAGER: III: CIFRAM: Distributed Computing Approaches for the Analysis of Enterprise and Systemic Risk using a Financial Contract-Based Infrastructure. We gratefully acknowledge the support of the National Science Foundation NSF Award 1445403 and the Office of Financial Research (OFR) in providing funding for this research.
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:
To support various financial calcualtions, the ACTUS (Algorithmic Contract Types Unified Standard) and and OpenGamma libraries can 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.
As we proceeded with the research on granular approaches toward understanding systemic risk, agent-based modeling and simulation (ABMS) became an important investigative method. ABMS is certainly a natural fit since the agents interact at a granular level. Agent-based modeling is based on the interactions of autonomous agents typically implemented with simple, but important behaviors encoded as algorithms. When done well, the interactions of many agents making simple but impactful decision leads to interesting emergent outcomes exhibited by complex systems. Our first applicaton focused on building agent-based models of the US corporate bond market (see below) to explore market dynamics, as well as assess regulatory approaches and policies. We are also working on agent-based models of the inter-bank loan market and continue to look for other interesting sectors. Trying to analyze and understand the interactions and data generated by agent-based simulations is a challenge in itself, so we have also been looking at data visualization and sonification techniques (check out our preliminary efforts to generate music from market movements). For more information, please see the project website at GSRisk.org and Don Berndt's research website at dberndt.link/home