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: 
2017 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. 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

More specifically the students will develop their understanding with respect to the following topics:

  • The time value of money and different ways to calculate compounding and discounting
  • Basic probability theory and common probability distributions
  • Standard statistical inferences such as point estimates, confidence intervals, hypothesis tests, and linear regressions.
  • Applicability and limitations of the standard statistical inferences.
  • Uncertainty in the financial market and simulation with uncertainty
  • The structured algorithmic way of thinking and performing specific tasks
Learning outcomes - Skills

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

  • Calculate compounding and discounting in different ways.
  • Calculate expectations, variances, and covariances of random variables
  • Calculate and interpret sample statistics such as mean, standard deviation, correlation, skewness, etc.
  • Construct point estimates and confidence intervals given sample
  • Conduct hypothesis tests given sample
  • Estimate and interpret linear regression models
  • Detect and remedy common violations of standard statistical inferences
  • Simulate financial models with uncertainty
  • Basic programming and code debugging in MATLAB
Learning Outcome - Reflection

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

Course content

Part I: Review topics

  1. Time value of money
  2. Probability theory
  3. Descriptive Statistics
  4. Sampling and Estimation
  5. 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. Introduction to Matlab
  2. Modelling uncertainty in financial markets
  3. Monte Carlo simulation
  4. Parameter selection
Learning process and requirements to students

Lectures (class participation and problem solving is essential).

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 itslearning or in the 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 start.

At resit, all exam components must, as a main rule, be retaken during next scheduled course.

Software tools
Matlab
Additional information

Excel and MATLAB are both needed for this course. Familiarity with Excel is expected before the course starts. Prior knowledge of MATLAB is not required before the course starts.

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.

Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA65151
Grading scale:
Point scale
Grading rules:
Internal and external examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
50No 1 Semester(s)Group/Individual ( 2 - 3)Assignments Group work (weekly or bi-weekly).
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA65151
Grading scale:
Point scale
Grading rules:
Internal and external examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
50Yes3 Hour(s)
  • All printed and handwritten support materials
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Individual Written examination with supervision
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:50
Invigilation:No
Grouping (size):Group/Individual (2-3)
Support materials:
Duration: 1 Semester(s)
Comment:Assignments Group work (weekly or bi-weekly).
Exam code: GRA65151
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:
  • All printed and handwritten support materials
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Duration:3 Hour(s)
Comment:Written examination with supervision
Exam code: GRA65151
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Exam organisation: 
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.