Statistical Learning for Quantitative Finance 2024

This workshop will give detailed insights into the latest techniques of using Statistical Machine-Learning techniques that are applied to Quantitative Finance. Tackling topics that arise in derivatives pricing, calibration and hedging, but also from time series management. This includes sophisticated modeling approaches for both the Q-quant and the P-quant. We give a thorough theoretical introduction and illustrate the concepts with concrete examples. Live demonstrations of the computational methods round up this course.

We also explain how to set up methods in Python using various libraries such as Numpy, SciKit Learn or Tensorflow/PyTorch. Our chosen examples are directly linked to relevant practical applications from Quantitative Finance and can be explored further after the course since all the material is available either as Python code or Jupyter notebooks.

This workshop will illustrate the application of state-of-the-art Statistical Learning applications for Quantitative Finance. By attending this course we aim to bring you to the next level, but please note that the theory of Natural Language Processing and GPT are not covered in this course.