FIN 3618 Financial Econometrics
FIN 3618 Financial Econometrics
Financial Econometrics can be understood as the application of statistical techniques using a digital tool (the software R) to answer questions in Finance. Therefore, Financial Econometrics can be used to test theories in Finance. As such, it is supports financial decision-making.
During the course students should develop knowledge of:
- 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.
- Make statistical inference (hypothesis testing and/or confidence intervals) on the parameter estimates of the model.
- Perform the various tests of the classical assumptions underlying OLS.
Moreover, the course provides students with the necessary skills to extract data digitally from Wharton Research Data Services (WRDS). The students will learn how to import the data into MATLAB, run econometric analyses, and produce tables or graphs summarizing their results which they then have to interpret using their knowledge about statistical techniques. To take advantage of the digital platform MATLAB, this course provides students with ample training to solve challenging problems in a digital way, e.g., using international stock return data.
In the course there will be focus on the assumptions underlying the different theories and methods covered. Hence, it is expected that students will have a critical attitude towards the realism of these. Upon completion of the course, the students should have a good understanding of the practical applicability of the theories and methods covered.
Introduction
- What is Financial Econometrics about?
- Types of data
- Returns in financial modelling
- Steps involved in formulating an econometric model
Mathematical and statistical foundations
- Sum and product notation
- Functions
- Differential calculus
- Matrices
- Probability and probability distributions
- Descriptive statistics
The classical linear regression model (CLRM)
- Regression versus correlation
- Simple regression
- Some further terminology
- The assumptions underlying the CLRM
- Properties of the OLS etsimator
- Precision and standard errors
- Statistical inference
- The exact significance level
Further development and analysis of the CLRM
- From simple to multiple linear regression
- Calculating the parameters in the generalized case
- The t-statistic
- Testing multiple hypothesis: the F-test
- Goodness of fit statistics
CLRM assumptions and the diagnostic tests
- Statistical distributions for diagnostic tests
- Assumption 1: Errors have zero mean
- Assumption 2: Errors have constant variance
- Assumption 3: Errors are linearly independent from each over time
- Assumption 4: Errors are linearly independent from x-variables at the same point in time
- Assumption 5: Errors are normally distributed
- Multicollinearity
- Adopting the wrong functional form
- Parameter stability tests
The course elements include lectures and two assignments. A class will typically start with a review of the last class.During the lectures, we will introduce new econometric techniques and discuss their practical application in MATLAB. 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 MATLAB, implement the econometric analyses in MATLAB, and summarize their results in tables or graphs. Here, strong emphasis will be placed on the statistical and economic interpretations of the results.
If a student misses a class, it is her/his responsibility to obtain any information provided in class that is not included on the course homepage/itslearning or in the text book.
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 in order 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 with reference to the letter grades when the course start.
At re-sit all exam components must, as a main rule, be retaken during next scheduled course.
.
Higher Education Entrance Qualification.
MET 2910 Mathematics and MET 2920 Statistics or equivalent.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 20 Grouping: Group/Individual (1 - 5) 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/Individual (1 - 5) 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 | 110 Hour(s) | |
Submission(s) | 30 Hour(s) | |
Student's own work with learning resources | 18 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.