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: 
2021 Spring
Active status: 
Active
Level of study: 
Master
Teaching language: 
English
Course type: 
One semester
Introduction

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 in order to be able to identify the empirical predictions of a financial or economic theory?
2) What do I need in order to be able to test the empirical predictions of the theory?
3) What do I need in order to be able to critically evaluate the research methodology used in financial research?
Answer: Research Methodology in Finance.

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. 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 MATLAB.

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 data

Time series modelling

  • Univariate time series analysis
    • Moving average (MA) processes
    • Autoregressive (AR) processes
    • ARMA processes
    • Box-Jenkings 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 modelling

  • Cointegration: Modelling long-run financial behaviour
    • Stationarity and unit root testing
    • Cointegration
    • Error correction models
    • Testing for cointegration
    • Parameter estimation in cointegrated systems
  • Modelling volatility: GARCH models
    • Models for volatility
    • Autoregressive conditionally heteroscedastic (ARCH) models
    • Generalised ARCH (GARCH) models
    • Estimation of GARCH models

Information search strategies

  • Search strategies
  • Literature review articles
  • Evaluation of sources
Teaching and learning activities

The course elements include lectures, in-class exercises, and assignments. A class will typically start with a review of the last class and the discussion of in-class exercises. During the lectures, we will introduce new econometric techniques and discuss their practical application in MATLAB. 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 MATLAB, run econometric analyses, and produce tables and graphs summarizing their findings in their solution paper. Here, strong emphasis will be placed on the statistical and economic interpretations of the results.

Software tools
Matlab
Additional information

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.

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.

Covid-19

Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.

Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA65476
Grading scale:
Point scale
Grading rules:
Internal examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
20No 1 Week(s)Group (3 - 4)Assignment 1
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA65476
Grading scale:
Point scale
Grading rules:
Internal examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
20No1 Week(s)Group (3 - 4)Assignment 2
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA65476
Grading scale:
Point scale
Grading rules:
Internal examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
10No 1 Week(s)Group/Individual ( 1 - 3)Assignment given by the library
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA65476
Grading scale:
Point scale
Grading rules:
Internal and external examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
50Yes2 Hour(s)
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Individual Final written examination under supervision
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:20
Invigilation:No
Grouping (size):Group (3-4)
Support materials:
Duration: 1 Week(s)
Comment:Assignment 1
Exam code:GRA65476
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:20
Invigilation:No
Grouping (size):Group (3-4)
Support materials:
Duration:1 Week(s)
Comment:Assignment 2
Exam code:GRA65476
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:10
Invigilation:No
Grouping (size):Group/Individual (1-3)
Support materials:
Duration: 1 Week(s)
Comment:Assignment given by the library
Exam code:GRA65476
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:50
Invigilation:Yes
Grouping (size):Individual
Support materials:
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Duration:2 Hour(s)
Comment:Final written examination under supervision
Exam code:GRA65476
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Type of Assessment: 
Continuous assessment
Grading scale: 
ECTS
Total weight: 
100
Student workload
ActivityDurationComment
Teaching
36 Hour(s)
Prepare for teaching
60 Hour(s)
Submission(s)
50 Hour(s)
Student's own work with learning resources
14 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.