Frontiers in Quantitative Finance
Machine Learning means new tools for the financial sector. They will be in focus at a workshop organized by the Department of Mathematical Sciences in collaboration with the Department of Finance at the Copenhagen Business School.
There has been significant development of the mathematical models applied in the financial sector. The Global Financial Crisis of 2007-2009 made it clear that the existing tools were flawed and often too simple. Concurrently there has been a sizeable development in numerical analysis and statistics – in particular in the field of machine learning.
These tools open up possibilities for new and better solutions to many of the classical problems in mathematical finance. In particular pricing, calibration and numerical solutions to optimization problems.
In addition, machine learning makes it possible to use more realistic models that before were not practical to use due to extensive computation times. In particular models with so-called rough volatility, give a more realistic description of how risk behaves over time, and they have so far been intractable due to their computational cost, making them hard to use in practice.
The workshop 14 November 2019 will present the latest research within the field, with local and international speakers from industry and academia.