Modeling Volatility Trading Using Econometrics and Machine Learning in Python
Modeling Volatility Trading Using Econometrics and Machine Learning in Python


How can market volatility be predicted, and what are the differences between heuristic models, econometric models and data science/machine learning models? This workshop provides lessons learned from doing econometric modeling in finance distilled into a training course with example project that compares the performance of turbulence, GARCH and blender algorithms. Particular focus on framing the problem and use the right tools for volatility modeling. Aimed at entry level finance quants who want a refresher on Python techniques or non-finance quants looking to make the leap into financial modeling.


Stephen Lawrence is the Head of Investment Management Fintech Data Science at The Vanguard Group. He oversees the integration of new structured and unstructured data sources into the investment process, leveraging a blend of NLP and predictive analytics. Prior to joining Vanguard, Dr. Lawrence was Head of Quantextual Research at State Street Bank where he lead a machine learning product team. Prior to that he led FX and Macro flow research for State Street Global Markets. Stephen holds a B.A. in Mathematics from the University of Cambridge and a Ph.D. in Finance from Boston College. He is also a TED speaker with a 2015 talk titled “The future of reading: it’s fast”.

Open Data Science




Open Data Science
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Cambridge, MA 02142

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