Data Science Day @ Columbia University

A key issue for regulators and the banking and financial service industries is mitigating  systemic large-scale counterparty risk.  Currently, individual financial institutions and regulators conduct systemic risk exposure analysis using proprietary models and data protocols absent any agreed upon baseline, best practices or public scrutiny. Without industry standards, shared benchmarks, or means to validate results, the impact of alternative policy interventions on the overall risk in the financial system remains uncertain.    The demo will illustrate new open source analytical tools that     1) Develop highly granular trade and counterparty cross-asset class risk simulation;   2) Aggregate at the counterparty level; and  3) Explore systemic linkages and changes under policy intervention scenarios.    Bringing large-scale open source risk models to the public domain will enable a standard-based approach that facilitates research and greater understanding of the impact that policy levers have on the financial system.