GRA 6547 Research Methodology in Finance
GRA 6547 Research Methodology in Finance
This course is of great importance for your master thesis for three reasons. First, it introduces you to econometric concepts that are used in empirical finance. As such, it helps you to understand the methodology emplyed in published articles. Second, this course equips you with the skills to test empirical predictations based on theories from finance or economics. Third, the library session familiarizes you with information search strategies.
The course kicks off with a brief revision of regression analysis and diagnostic tests, before delving into panel regressions. Subsequently, we shift our focus to univariate and multivariate time series models. In the section about cointegration, we explore unit roots, stationarity tests, and error correction models. Lastly, GARCH models are employed to capture volatility clustering.
For every topic, there will be R codes showcasing the practical implementation of each econometric technique or concept. Usually, each R code is accompanied by a video that walks you line-by-line through the code. In the context of the mid-term exam, you need to demonstrate your coding skills by applying econometric techniques to real-world data.
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
- 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
- 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.
It is the student’s own responsibility to obtain any information provided in class.
Additional support materials for the 30% mid-term exam
Students are allowed to bring a printout of a PDF file generated using the “compile report” function in RStudio. This printout must contain only the R code and the corresponding output used to solve the pre-released tasks. The use of R Markdown or any form of additional annotations, formatting, or explanatory text is strictly prohibited.
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.
This is a mandatory course for four different programmes. For MSc in Quantitative Finance students, there are no prerequisites. For other 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 6034 Investments is 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: Mid-term exam. Individual structured test under supervision based on group work during the semester. ADDITIONAL SUPPORT MATERIALS: Students are allowed to bring a printout of a PDF file generated using the “compile report” function in RStudio. This printout must contain only the R code and the corresponding output used to solve the pre-released tasks. The use of R Markdown or any form of additional annotations, formatting, or explanatory text is strictly prohibited. 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) Comment: Final written examination under supervision 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 | 24 Hour(s) | Lectures (synchronous teaching) |
Digital resources | 12 Hour(s) | Asynchronous learning activties |
Prepare for teaching | 30 Hour(s) | Reading textbook-chapter (as marked on the syllabus) prior to attending class, including viewing videos. |
Student's own work with learning resources | 30 Hour(s) | |
Group work / Assignments | 35 Hour(s) | Library assignment & students are given a group work during the semester than will end up with an individual (structured) test. |
Examination | 30 Hour(s) | Exam including preparations |
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.
