A Data Science Approach to Systemic Risk


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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++. Slides: https://github.com/niknow/pydata-london-2018-systemic-risk

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