FIN 3618 Financial Econometrics
FIN 3618 Financial Econometrics
Financial econometrics can be understood as the application of statistical techniques to data using the programming language R (which is widely used in the financial industry) to answer questions in finance. Therefore, financial econometrics can be used to test theories in finance. As such, it supports financial decision-making.
During the course, students should develop knowledge about:
- The concept of regression analysis using Ordinary Least Squares (OLS).
- Statistical inference within the regression model.
- How violations of the classical assumptions underlying OLS affect the regression model.
Upon completion of the course, the students should be able to:
- Estimate regression models using OLS.
- Perform hypothesis tests on the parameter estimates of the regression model.
- Test the assumptions underlying the classical linear regression model.
Moreover, the course provides students with the necessary skills to extract data from Wharton Research Data Services (WRDS) and other relevant data sources (e.g., Yahoo finance, FRED, or Qualcomm) and to implement the econometric techniques in the programming language R (which is widely used in the financial industry).
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.
Introduction and mathematical foundations
- Steps involved in formulating an econometric model
- Functions
- Matrices
Statistical foundations and dealing with data
- Probability and probability distributions
- Descriptive statistics
- Types of data and data aggregation
- Simple returns vs. log returns
Review: The capital asset pricing model (CAPM)
- The assumptions underlying the CAPM
- Security market line
- Capital market line
- Sharpe ratio, Treynor ratio, Jensen's alpha
The classical linear regression model (CLRM)
- Simple regression
- The assumptions underlying the CLRM
- Properties of the OLS estimator
- Standard errors
- Statistical inference
- t-statistic
- p-value
Further development and analysis of the CLRM
- From univariate to multivariate regression
- Parameter estimation and standard errors in the multivariate regression framework
- Testing multiple hypotheses: the F-test
- R² and adjusted R²
CLRM assumptions and the diagnostic tests
- Assumption 1: Errors have zero mean
- Assumption 2: Errors have constant variance
- Assumption 3: Errors are linearly independent of each other
- Assumption 4: Errors are linearly independent of x-variables
- Assumption 5: Errors are normally distributed
- Multicollinearity
- Omitted variable bias
- Parameter stability tests
The course elements include lectures, in-class exercises, and two assignments. During the lectures, we will introduce new econometric techniques and discuss their practical application in R. To strengthen the students' understanding of these concepts, the students have to submit two assignments (group work). In these, they download the data themselves from a database (e.g., WRDS), import the data into R, implement the econometric analyses in R, and summarize their 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.
This is a course with continuous assessment (several exam components) and one final exam code. Each exam component is graded by using points on a scale from 0-100. The components will be weighted together according to the information in the course description to calculate the final letter grade for the examination code (course). Students who fail to participate in one/some/all exam elements will get a lower grade or may fail the course. You will find detailed information about the point system and the cut-off points regarding the letter grades when the course starts.
In the case of exam components that have to be solved in groups, the course responsible decides on the allocation of the students. At re-sit, all exam components must, as a main rule, be retaken during the next scheduled course.
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 |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 20 Grouping: Group (1 - 4) Duration: 1 Week(s) Comment: Assignment 1 Exam code: FIN36181 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
Exam category: Submission Form of assessment: Written submission Weight: 20 Grouping: Group (1 - 4) Duration: 1 Week(s) Comment: Assignment 2 Exam code: FIN36181 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
Exam category: Submission Form of assessment: Written submission Invigilation Weight: 60 Grouping: Individual Support materials:
Duration: 2 Hour(s) Comment: Final written examination with supervision Exam code: FIN36181 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
| Activity | Duration | Comment |
|---|---|---|
Teaching | 42 Hour(s) | |
Prepare for teaching | 75 Hour(s) | |
Group work / Assignments | 30 Hour(s) | |
Student's own work with learning resources | 53 Hour(s) |
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.