Enabling Data Analysis for Addressing Systemic Risk

Recently, the U.S. experienced an economic crisis that shook confidence in key aspects of the financial system, and led to some calls for changes in the way the government tracks economic information that might warn of such a crisis. Among those changes was the creation of the Office of Financial Research (OFR), intended to collect and provide information to "anticipate emerging threats to financial stability or assess how shocks to one financial firm could impact the system as a whole" [OFR 2010]. These functions have been termed systemic risk: the risk that a threat to a large, single component of the financial system poses to the system as a whole, due to the interconnectedness of the system and potential lack of consumer confidence in the system that might be caused if one component failed. This paper considers the computational approaches that may be needed in support of the mission of providing information about systemic risk, and possible mitigations of that risk. We acknowledge that there are many schools of thought for why the recent crisis occurred, the degree of systemic risk it posed, and possible government actions to mitigate the risk. Our position is that an agency such as the OFR with responsibility for monitoring systemic risk must be prepared to analyze diverse, uncertain information about the financial system and threats to it. Such an agency must be prepared to evaluate this information from multiple perspectives, and assess possible future outcomes given a variety of assumptions and regulatory responses.