GRA 6547 Research Methodology in Finance
GRA 6547 Research Methodology in Finance
Welcome to this mandatory and important research methodology course in Finance. The importance of this course can be summarised in the following three questions:
1) What do I need to be able to identify the empirical predictions of a financial or economic theory?
2) What do I need to be able to test the empirical predictions of the theory?
3) What do I need to be able to critically evaluate the research methodology used in financial research?
Answer: Research Methodology in Finance.
During the course, students should develop knowledge about:
- Regression analysis,
- Time series modelling,
- Cointegration and volatility modelling, and
- information search strategies.
Upon completion of the course, the students should be able to:
- Estimate regression models, perform hypotheses tests on parameter estimates, and test for violations of the underlying assumptions.
- Estimate univariate and multivariate time series models and identify which model is most suitable for a given data series.
- Construct models capturing the long-run relations between cointegrated variables and estimate univariate GARCH models.
- Search for articles on a given topic and evaluate their sources critically.
Moreover, the course provides students with the necessary skills to extract data from Wharton Research Data Services (WRDS) and implement the econometric techniques in R.
This course introduces students to important econometric techniques that are used in empirical finance. It focusses 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.
Regression analysis
- Classical linear regression model (CLRM)
- CLRM assumptions and the diagnostic tests
- Panel regressions
Time series modeling
- Univariate time series analysis
- Moving average (MA) processes
- Autoregressive (AR) processes
- ARMA processes
- Box-Jenkins methodology
- Forecasting in econometrics
- Multivariate time series analysis
- Vector autoregressive (VAR) models
- Block significance and causality tests
- Impulse responses and variance decompositions
Cointegration and volatility modeling
- Cointegration: Modelling long-run financial behavior
- Stationarity and unit root testing
- Cointegration
- Error correction models
- Testing for cointegration
- Parameter estimation in cointegrated systems
- Modeling volatility: GARCH models
- Models for volatility
- Autoregressive conditionally heteroscedastic (ARCH) models
- Generalized ARCH (GARCH) models
- Estimation of GARCH models
Information search strategies
- Search strategies
- Literature review articles
- Evaluation of sources
The course elements include lectures, in-class exercises, and assignments. A class will typically start with a review of the last class and a discussion of in-class exercises. 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, run econometric analyses and produce tables and graphs summarizing their findings in their solution paper. Here, strong emphasis will be placed on the interpretation of the results from a statistical as well as from an economic point of view.
Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.
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 starts.
At resit, all exam components must, as a main rule, be retaken during next scheduled course.
All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 20 Grouping: Group (1 - 5) Duration: 1 Semester(s) Comment: Two group assignments, each carrying a weight of 10%. Exam code: GRA 65476 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: 10 Grouping: Group/Individual (1 - 3) Duration: 1 Week(s) Comment: Assignment given by the library Exam code: GRA 65476 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: 70 Grouping: Individual Support materials:
Duration: 2 Hour(s) Comment: Final written examination under supervision Exam code: GRA 65476 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 | 36 Hour(s) | |
Prepare for teaching | 50 Hour(s) | |
Group work / Assignments | 50 Hour(s) | |
Student's own work with learning resources | 24 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.