Portfolio Optimization for Cointelated Pairs: SDEs vs Machine Learning

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With the recent rise of Machine Learning as a candidate to partially replace classic Financial Mathematics methodologies, Cristin Buescu discusses the performances of both in solving the problem of dynamic portfolio optimization in continuous-time, finite-horizon setting for a portfolio of two assets that are intertwined. Image courtesy of Cristin Buescu.

Image courtesy of the interviewee


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