Machine learning and numerical techniques in financial econometrics

This course gives an overview of the newest techniques within financial econometrics; GMM estimation, Hansen Jaganathan bound and distrances, machine learning with regularization regression, regularisation with GMM, simulation methods in estimation, deep learning, advanced univariate and multivariate garch models, MCMC estimation and filtering, advanced PCA analysis and estimation.

Give students "state of the art" knowledge of machine learning and numerical techniques applied in financial econometrics/empirical finance.

The course consist of lectures from the teachers as well as exercises and presetation of termpapers by the students. Students must participate by presentation of exercises and termpaper during the seminars.