GRA 6547 Research Methodology in Finance

GRA 6547 Research Methodology in Finance

Course code: 
GRA 6547
Department: 
Finance
Credits: 
6
Course coordinator: 
Patrick Konermann
Course name in Norwegian: 
Research Methodology in Finance
Product category: 
Master
Portfolio: 
MSc - Core course
Semester: 
2025 Spring
Active status: 
Active
Level of study: 
Master
Teaching language: 
English
Course type: 
One semester
Introduction

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. 

Learning outcomes - Knowledge

During the course, students should develop knowledge about:

  • Regression analysis,
  • Time series modelling,
  • Cointegration and volatility modelling, and
  • Information search strategies.
Learning outcomes - Skills

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.

General Competence

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. 

Course content

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
Teaching and learning activities

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.

Software tools
R
R/R-Studio
Additional information

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.

Qualifications

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.

Required prerequisite knowledge

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
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 6547x
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: 
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Duration: 
3 Hour(s)
Comment: 
Individual structured test under supervision based on group work during the semester
Exam code: 
GRA 6547x
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: 
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Duration: 
3 Hour(s)
Exam code: 
GRA 6547x
Grading scale: 
ECTS
Resit: 
Examination when next scheduled course
Type of Assessment: 
Ordinary examination
All exams must be passed to get a grade in this course.
Total weight: 
100
Student workload
ActivityDurationComment
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)
Sum workload: 
160

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.