Course - Machine learning and numerical techniques in financial econometrics - IØ8816
Machine learning and numerical techniques in financial econometrics
Choose study yearLessons are not given in the academic year 2024/2025
About
About the course
Course content
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.
Learning outcome
Give students "state of the art" knowledge of machine learning and numerical techniques applied in financial econometrics/empirical finance.
Learning methods and activities
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.
Recommended previous knowledge
Courses similar to TIØ4145 Corporate Finance, TIØ4317 Empirical and Quantitative Methods in Finance, and TIØ4140 Project Evaluation and Financing.
Required previous knowledge
Knowledge of finance and economics, statistics/econometrics, datahandling, and programming at graduate/master level.
Course materials
Books and articles by Eric Ghysel.
Subject areas
- Managerial Economics, Finance and Operations Research
- Industrial Economics and Technology Management
- Business Economics
- Financial Economics
Contact information
Department with academic responsibility
Department of Industrial Economics and Technology Management