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2019/2020  KAN-COECO1056U  Financial Econometrics

English Title
Financial Econometrics

Course information
Language English
Course ECTS 7.5 ECTS
Type Mandatory offered as elective
Level Full Degree Master
Duration One Semester
Start time of the course Spring
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 50
Study board
Study Board for MSc in Advanced Economics and Finance
Course coordinator
  • Lisbeth La Cour - Department of Economics (ECON)
Main academic disciplines
  • Finance
  • Statistics and quantitative methods
  • Economics
Teaching methods
  • Blended learning
Last updated on 16-03-2020

Relevant links
Learning objectives
  • Understand Time Series Econometric models for inference and predictions within macroeconomic and financial applications
  • Interpret and analyze estimation output within a Time Series framework.
  • Relate Time Series Econometric Code and output in econometric packages
Course prerequisites
This is a mandatory course for the MSc in Advanced Economics and Finance. It is assumed that students have knowledge similar to the course KAN-COECO1058U Econometrics .

The course has 60 contact hours and there is a high level of interaction between lecturer and students, and in general a high work load.

To sign up send a 1-page motivational letter, a 1-page CV, and a grade transcript to ily.stu@cbs.dk before the registration deadline for elective courses. You may find the registration deadlines on my.cbs.dk ( https:/​/​studentcbs.sharepoint.com/​graduate/​pages/​registration-for-electives.aspx ).

Please also remember to sign up through the online registration.
Examination
The exam in the subject consists of two parts:
Financial Econometrics - midterm:
Sub exam weight50%
Examination formHome assignment - written product
Individual or group examIndividual exam
Size of written productPlease see text below
There is no max., but you have two hours to work on your exam answer
Assignment typeWritten assignment
DurationWritten product to be submitted on specified date and time.
Grading scale7-point grading scale
Examiner(s)One internal examiner
Exam periodSpring
Make-up exam/re-exam
Same examination form as the ordinary exam
Financial Econometrics - final:
Sub exam weight50%
Examination formHome assignment - written product
Individual or group examIndividual exam
Size of written productMax. 10 pages
Assignment typeProject
DurationWritten product to be submitted on specified date and time.
Grading scale7-point grading scale
Examiner(s)One internal examiner
Exam periodSummer
Make-up exam/re-exam
Same examination form as the ordinary exam
Description of the exam procedure

The project will consist of an empirical paper based on data that is selected by each students. The empirical analysis must demonstrate that the student can choose relevant models and use them to estimate, interpret and possibly forecast (or use the models in other relevant ways) on their chosen data. Fulfilling the other learning objectives of the course will also be important. The student will be asked to use the statistical software offered for the course for their analysis. There will be no supervision of the projects but simple questions that prevent student from not moving on with their analysis can be answered during office hours. Students are allowed to choose their data from the beginning of the course.
Course content, structure and pedagogical approach

The aim of the course is to provide the students with an understanding of models, estimation methods within the field of time series econometrics. The course will provide both a theoretical and an applied (hands on) angle on the topic. Practical aspects will be discussed and statistical properties will be proved. Linear, conditional heteroskedasticity, and multivariate models will be considered. The  course builds on a standard introductory course in econometrics. Knowledge of fundamental concepts of mathematics and statistics is required. The students need to be familiar with matrix notation.
Description of the teaching methods
Lectures and computer based exercise classes.

Please bring a laptop to all classes and labs.
Feedback during the teaching period
Office Hours and feedback in class after midterm exam.
Student workload
Lectures/Exercise classes 60 hours
Exam 6 hours
Preparation 140 hours
Further Information

Part of this course may also be taken as a PhD course for a limited number of PhD students.
Expected literature

Indicative:

 

Please see LEARN before course start.
Last updated on 16-03-2020