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A Data Science Approach to Systemic Risk


We present data science approach to evaluate, predict and optimize financial regulation. As the trade data of the real financial system is proprietary, we use random graph generation to produce a dataset of simulated financial systems and study the impact of prominent regulations like Initial Margin over time. This uses various open source technologies in Python and C++.


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