FIN 3618 Financial Econometrics
FIN 3618 Financial Econometrics
Financial Econometrics is designed to help students understand and apply statistical techniques to financial data. Students will gain valuable analytical and programming skills that will enhance their ability to conduct research and perform financial analysis in industry settings. The course will center on linear regression, which is the most commonly used statistical technique in finance. It will provide a strong foundation for anyone interested in pursuing a quantitative role in the financial sector.
During the course, students should develop knowledge about:
- Regression analysis, which is frequently used to analyze financial data.
- Statistical inference, which can be used to draw conclusions about populations using a sample of data.
- Common issues and practices with financial data.
Upon completion of the course, the students should be able to:
- Estimate regression models.
- Perform hypothesis tests on the parameter estimates of the regression model.
- Test the assumptions underlying the classical linear regression model.
- Extract data from databases (e.g WRDS) and implement econometric techniques in the R programming language.
In the course, the focus will be on the assumptions underlying the different theories and methods covered. Hence, it is expected that students have a critical attitude towards the realism of these.
1. Review of the mathematical and statistical foundations of financial econometrics.
2. Overview of the collection, transformation, and interpretation of financial data.
3. The capital asset pricing model (CAPM), which will be used in econometric exercises throughout the course.
4. Linear regression and its extensions, which form the foundation of financial econometrics.
5. Diagnostic tests for problems in linear regression.
The course elements include lectures, in-class exercises, online exercises, a graded group assignment, and a final exam. During the lectures, we will introduce new econometric techniques and discuss their practical application in R. To strengthen the students' understanding of these concepts, they will have to submit a group assignment. This will involve downloading the data themselves from a database (e.g., WRDS), importing the data into R, implementing the econometric analyses in R, and summarizing the results in tables or graphs. Here, strong emphasis will be placed on the interpretations of the results from a statistical as well as from an economic point of view.
Higher Education Entrance Qualification
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
MET 2910 Mathematics and MET 2920 Statistics or equivalent.
| Assessments |
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Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Weight: 40 Grouping: Group (1 - 4) Duration: 1 Semester(s) Exam code: FIN 36182 Grading scale: ECTS Resit: Examination every semester |
Exam category: School Exam Form of assessment: Structured Test Exam/hand-in semester: First Semester Weight: 60 Grouping: Individual Support materials:
Duration: 2 Hour(s) Comment: Final examination. All exams must be passed to obtain a final grade in the course. Exam code: FIN 36183 Grading scale: ECTS Resit: Examination every semester |
All exams must be passed to get a grade in this course.
| Activity | Duration | Comment |
|---|---|---|
Teaching | 30 Hour(s) | |
Prepare for teaching | 75 Hour(s) | |
Group work / Assignments | 30 Hour(s) | |
Student's own work with learning resources | 50 Hour(s) | |
Digital resources | 15 Hour(s) | Student’s own work with learning platform material |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.
All exams must be passed to obtain a final grade in the course.