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: 
2019 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

The aim of this course is to introduce students to important econometric techniques that are used in empirical Finance and to facilitate an awareness in students of how these techniques can be applied. After completing this course, you should be able to employ and understand most of the research methodology used in today's published research in empirical Finance.

More specifically, you should:

  • have an advanced knowledge of the principles and methods of modern financial econometrics;
  • have extended and deepened your understanding of Econometrics gained in your basic Econometrics course and improved your critical judgement and discimination in the choice of techniques applicable to complex situations;
  • have extended your understanding of the application of econometric methods and interpretation of the results at an advanced level;
  • have further practiced problem solving skills at an advanced level and the use of econometric software.

Understanding of information search strategies:

  • acquaintance with methods for information "harvesting" and search techniques;
  • know what a critical literature review is and how this type of articles may be searched for and used;
  • critical evaluation of sources.
Learning outcomes - Skills

You should have developed further the following key skills:

  • written communication,
  • oral communication,
  • ethical awareness in conducting research,
  • teamwork,
  • problem solving and analysis,
  • using initiative, and 
  • computer literacy.
Learning Outcome - Reflection

In the course, we will focus on the assumptions underlying the different theories and methods covered. Hence, it is expected that students will have a critical attitude towards the realism of these. Upon completion of the course, the students should have a good understanding of the practical applicability of the theories and methods covered.

Course content

This course introduces students to modern econometric techniques that are relevant for empirical research in Finance. The course starts with a session on data gathering in the library. Then univariate time series models and forecasting are covered before we move on to multivariate time series models and cointegration. The focus then switches to the modeling of volatility.

Course Content:

  1. Data gathering
  2. Review of the classical linear regression model
  3. Univariate time series analysis
  4. Multivariate time series analysis
  5. Cointegration: Modeling long-run financial behaviour
  6. Modelling Volatility: GARCH models
  7. Information search strategies and source evaluation

Each topic will be accompanied by hands-on practical applications of empirical Finance topics. 

During the semester there will be thesis seminars to guide the students towards writing a thesis registration form. This is conducted outside the course.

Learning process and requirements to students

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 that is not included on the course homepage/itslearning or text book.

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.

Software tools
Matlab
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.

Assessments
Assessments
Exam category: 
Submission
Form of assessment: 
Written submission
Weight: 
20
Grouping: 
Group/Individual (1 - 5)
Duration: 
1 Week(s)
Comment: 
Assignment 1
Exam code: 
GRA65476
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: 
20
Grouping: 
Group/Individual (1 - 5)
Duration: 
1 Week(s)
Comment: 
Assignment 2
Exam code: 
GRA65476
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: 
GRA65476
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: 
50
Grouping: 
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 leading to ECTS letter grade
Resit: 
All components must, as a main rule, be retaken during next scheduled course
Exam organisation: 
Continuous assessment
Grading scale: 
ECTS
Total weight: 
100
Student workload
ActivityDurationComment
Teaching
36 Hour(s)
Prepare for teaching
50 Hour(s)
Submission(s)
40 Hour(s)
Student's own work with learning resources
34 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.