# 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:
2020 Autumn
Active status:
Active
Level of study:
Master
Teaching language:
English
Course type:
One semester
Introduction

This course lays foundation for understanding of quantitative methods most commonly used in finance worldwide. These include time value of money, statistical concepts, probability concepts, hypothesis testing, regression analysis, and simulation methods. This course also introduces MATLAB programming, an essential programming language that will be used in the rest of the programme.

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, its 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 and code debugging in MATLAB with the 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 MATLAB.

Course content

Part I: Review

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 Matlab

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 the quantitative data analysis and discuss their applications using practical examples. To strengthen the 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 MATLAB. This allows the students to structure the data analysis tasks into smaller consecutive steps and develop the algorithms that execute these steps. Here, the strong emphasis will be placed on statistical and economic interpretations of the results. Since mastering of a programming language requires practice, the 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. Each assignment will cover one of the topics and will be graded on the pass (100 pts) or fail (0 pts) scale. Finally, the course includes a graded mid-term exam that provides the students with interim assessment of their standing.

Software tools
Matlab
Additional information

MATLAB will be used as the main analytical tool. Prior knowledge of MATLAB is not required before the course starts.

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.

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.

### 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.

Assessments
Assessments
Exam category:
Submission
Form of assessment:
Written submission
Weight:
20
Grouping:
Group/Individual (1 - 2)
Duration:
1 Semester(s)
Comment:
Small assignments during the semester.
Individual or group work.
Exam code:
GRA65151
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:
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:
GRA65151
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:
GRA65151
Grading scale:
Point scale leading to ECTS letter grade
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
Sum workload:
0

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.