FIN 3618 Financial Econometrics
FIN 3618 Financial Econometrics
Financial Econometrics can be understood as the application of statistical techniques to answer questions in finance. Financial Econometrics can therefore be used to test theories in finance. As such, it is supports financial decision-making.
During the course students should develop knowledge of:
- The role of Financial Econometrics in financial research.
- The steps involved in formulating, estimating, and evaluating an econometric model.
- The role of descriptive statistics.
- The difference between correlation and regression.
- The concept of regression analysis using Ordinary Least Squares (OLS).
- Statistical inference within the regression model.
- How violations of the classical assumptions under lying OLS affect the regression model.
- Different model mis-specifications and biases.
- The concept of parameter stability/structural breaks.
Upon completion of the course the students should be able to:
- Estimate and interpret descriptive statistics for the variables used in the model of interest.
- 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.
- Identify potential mis-specifications and biases.
- Perform parameter stability tests (test for structural breaks).
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. The students should upon completion of the course have a good understanding of the practical applicability of the theories and methods covered.
Introduction
- What is Financial Econometrics about
- Types of financial data
- Returns in financial modelling
- Steps involved in formulating an econometric model
Mathematical and statistical foundations
- Functions
- Matrices
- Probability and probability distributions
- Descriptive statistics
The classical linear regression model
- Regression versus correlation
- Simple regression
- The assumptions underlying the classical linear regression model
- Properties of the OLS etsimator
- Precision and standard errors
- Statistical inference
Further development and analysis of the classical linear regression model
- Multiple regression
- Testing multiple hypothesis: the F-test
- Goodness of fit statistics
Classical linear regression model assumptions and the diagnostic tests
- Heteroscedasticity
- Autocorrelation
- Non-stochastic explanatory variables
- Multicolinearity
- Specification mistakes and biases
- Parameter stability tests
A class will typically consist of a review of the last class, a lecture introducing new material and exercises that are solved by the lecturer or students. Each main topic will be accompanied by a hands-on practical application of an empirical Finance topic. The software package MATLAB will be an integral part of the coursework.
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.
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Higher Education Entrance Qualification.
MET 2910 Mathematics and MET 2920 Statistics or equivalent.
| Assessments |
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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.