course-details-portlet

IØ8816 - Machine learning and numerical techniques in financial econometrics

About

Lessons are not given in the academic year 2024/2025

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.

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.

More on the course

No

Facts

Version: 1
Credits:  2.5 SP
Study level: Doctoral degree level

Coursework

No

Language of instruction: English

Location: Trondheim

Subject area(s)
  • 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

Timetable

List view
Calendar view

Examination

  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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