GRA 6547 Research Methodology in Finance
GRA 6547 Research Methodology in Finance
This course is of great importance for your master thesis. It will provide you with the knowledge and skills to test empirical predictions of theories from finance or economics and critically assess the methodology employed in research papers. Additionally, the library session will familiarize you with information search strategies.
In class, we will introduce econometric concepts and discuss the intuition behind them. You will learn how to implement these econometric techniques in R and then you need to apply them to real-world data, in an assignment.
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 focuses 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
- Granger 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
- Modeling volatility: GARCH models
- Models for volatility
- Autoregressive conditionally heteroscedastic (ARCH) models
- Generalized ARCH (GARCH) models
- Maximum likelihood estimation
Information search strategies
- Search strategies
- Literature review articles
- Evaluation of sources
The course elements include lectures, in-class exercises, and an assignment. 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 an assignment (group work). 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.
The exam for this course has been changed starting academic year 2023/2024. The course now has two ordinary exams. It is not possible to retake the old version of the exam. For questions regarding previous results, please contact InfoHub.
It is the student’s own responsibility to obtain any information provided in class.
Honour Code
Academic honesty and trust are important to all of us as individuals and represent values that are encouraged and promoted by the honor code system. This is a most significant university tradition. Students are responsible for familiarizing themselves with the ideals of the honor code system, to which the faculty are also deeply committed. Any violation of the honor code will be dealt with in accordance with BI’s procedures for cheating. These issues are a serious matter to everyone associated with the programs at BI and are at the heart of the honor code and academic integrity. Please ask if you have any questions about your responsibilities under the honor code.
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.
For the MSc in Quantitative Finance, there is no prerequisite. For the other BI MSc programmes, the most important prerequisite is either GRA 6515 Quantitative Methods for Finance (for MSc in Finance and MSc in Sustainable Finance students) or GRA 6039 Econometrics with Programming (for MSc in Business students with a major in Finance). Further, GRA 6035 Mathematics and GRA 6034 Investments are strongly recommended.
| Assessments |
|---|
Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Grouping: Group/Individual (1 - 3) Duration: 1 Week(s) Comment: Library assignment Exam code: GRA 65477 Grading scale: Pass/fail Resit: Examination when next scheduled course |
Exam category: School Exam Form of assessment: Structured Test Exam/hand-in semester: First Semester Weight: 30 Grouping: Individual Support materials:
Duration: 3 Hour(s) Comment: Individual structured test under supervision based on group work during the semester Exam code: GRA 65478 Grading scale: ECTS Resit: Examination when next scheduled course |
Exam category: School Exam Form of assessment: Written School Exam - pen and paper Exam/hand-in semester: First Semester Weight: 70 Grouping: Individual Support materials:
Duration: 3 Hour(s) Exam code: GRA 65479 Grading scale: ECTS Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this course.
| Activity | Duration | Comment |
|---|---|---|
Teaching | 36 Hour(s) | |
Group work / Assignments | 28 Hour(s) | |
Examination | 6 Hour(s) | |
Student's own work with learning resources | 30 Hour(s) | |
Prepare for teaching | 60 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.