GRA 6515 Quantitative Methods for Finance

GRA 6515 Quantitative Methods for Finance

Course code:
GRA 6515
Department:
Finance
Credits:
6
Course coordinator:
Tatyana Marchuk
Course name in Norwegian:
Quantitative Methods for Finance
Product category:
Master
Portfolio:
MSc in Finance
Semester:
2023 Autumn
Active status:
Active
Level of study:
Master
Teaching language:
English
Course type:
One semester
Introduction

This course lays a foundation for the understanding of quantitative methods most commonly used in finance worldwide. These include statistics, probability theory, hypothesis testing, regression analysis, and simulation methods. This course also introduces R programming, an essential programming language that will be used in the rest of the program.

Learning outcomes - Knowledge

In this course the students will develop their understanding with respect to two important areas of quantitative analysis:

• Preliminary data analysis using the standard statistical inferences, their applicability, and limitations.
• The structured algorithmic way of thinking about financial data analysis and modelling
Learning outcomes - Skills

During the acquisition of the above-mentioned knowledge the students will acquire the following skills:

• Estimation and interpretation of statistical models using both real and simulated data
• Basic programming in R with an emphasis on data processing, statistical analysis, and simulation of economic processes
General Competence

The acquired theoretical and practical knowledge provided by the course should enable the students to apply and understand the standard statistical methods to analyze financial data. Further, the students should acquire basic programming skills in R.

Course content

Part I: Review Topics

1. Probability theory
2. Descriptive statistics
3. Sampling and estimation
4. Hypothesis testing

Part II: Regression analysis

1. Simple linear regression
2. Multivariate regression
3. Issues in regression analysis

Part III: Modelling uncertainty with R

1. Modelling uncertainty in financial markets
2. Monte Carlo simulations
Teaching and learning activities

The course elements include lectures, practice exercises, and take-home assignments. During the lectures, we will cover the key theoretical concepts of quantitative data analysis and discuss their applications using practical examples. To strengthen their understanding of the material, the students are asked to solve and discuss in-class exercises. Therefore, class participation and problem-solving are essential to achieving the learning objectives.

The implementation of the statistical methodology is supported by programming exercises in R. This allows the students to structure the data analysis tasks into smaller consecutive steps and develop the algorithms that execute these steps. Here, strong emphasis will be placed on statistical and economic interpretations of the results. Since mastering a programming language requires practice, further learning activities are encouraged by hands-on programming sessions, various online tutorials, and replication exercises.

The students will be able to assess their progress in achieving the learning objectives using several individual take-home assignments.  Finally, the course includes a graded mid-term exam that provides the students with an interim assessment of their standing.

Software tools
R
R/R-Studio

The exam for this course has been changed starting academic year 2023/2024. The course now has two exam codes instead of one. It is not possible to retake the old version of the exam. Please note new exam codes in the Exam section of the course description.

It is the student’s own responsibility to obtain any information provided in class.

Software tools
​R will be used as the main analytical tool. Prior knowledge of R is not required before the course starts but students are encouraged to attend introductory lectures on R and install the required software before the course start.

Honour Code
Academic honesty and trust are important to all of us as individuals and represent values that are encouraged and promoted by the honour code system. This is a most significant university tradition. Students are responsible for familiarizing themselves with the ideals of the honour code system, to which the faculty are also deeply committed. Any violation of the honour code will be dealt with in accordance with BI’s procedures for cheating. These issues are a serious matter to everyone associated with the programmes at BI and are at the heart of the honour code and academic integrity. If you have any questions about your responsibilities under the honour code, please ask.

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.

Assessments
Assessments
Exam category:
Submission
Form of assessment:
Written submission
Invigilation
Weight:
30
Grouping:
Individual
Support materials:
• BI-approved exam calculator
• Simple calculator
• Bilingual dictionary
Duration:
2 Hour(s)
Comment:
Mid-term examination.
Written examination under supervision
Exam code:
GRA 65152
ECTS
Resit:
Examination when next scheduled course
Exam category:
Submission
Form of assessment:
Written submission
Invigilation
Weight:
70
Grouping:
Individual
Support materials:
• BI-approved exam calculator
• Simple calculator
• Bilingual dictionary
Duration:
3 Hour(s)
Comment:
Final written examination under supervision
Exam code:
GRA 65153