GRA 6561 Computational Methods

GRA 6561 Computational Methods

Course code: 
GRA 6561
Department: 
Finance
Credits: 
6
Program of study: 
Master of Science in Quantitative Finance
Course coordinator: 
Chunyu Yang
Product category: 
Master
Portfolio: 
MSc in Quantitative Finance
Semester: 
2019 Autumn
Active status: 
Active
Teaching language: 
English
Course type: 
One semester
Introduction

This course covers the advanced computational methods and their applications in Finance. It starts with the lattice methods and then moves to Monte Carlo simulation and dynamic programming. It finishes with an introduction to C++ or Python programming. These methods will be applied on both standard and exotic derivative instruments and other Finance applications with emphasis on the appropriate methods for each.

Learning outcomes - Knowledge

After taking the course, students should know:

  • Advanced computational methods that are widely used in Finance academia and industry, such as lattice/tree methods, simulation methods, and dynamic programming.
  • Common programming elements such as for loops, if statements, variables, arrays, input, output, plot, and debug.
Learning outcomes - Skills

After taking the course, students should be able to:

  • Apply computational methods to applications in Finance, such as derivative pricing, real option valuation, and dynamic portfolio choice.
  • Do programming in Matlab, C++ or Python.
General Competence
  • Understanding of quantitative models and ability to implement quantitative methods.
Course content
  • Ethics and sustainability in quantitative finance
  • Basic concepts in numerical methods
  • Lattice/Tree methods
    • Binomial and trinomial one-dimensional trees for geometric Brownian motion
    • Recombining trees for mean-reverting processes
    • Trees for higher dimensional processes
    • Pricing vanilla options: European and American options
    • Pricing exotic options: Asian options, barrier options, swing options (if time allows), etc
  • Simulation
    • Simulating dynamics of returns
    • Variance reduction
    • Valuation through simulation: energy projects, MBS baskets, etc.
  • Dynamic programming (if time allows)
  • Introduction to C++ or Python programming (if time allows)
Teaching and learning activities

The course will be organized as a mixture of lectures, presenting the theory and methods, in-class examples and hands-on implementations of the various methods and tools.

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.

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 programs 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.

Exam categoryWeightInvigilationDurationGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA65611
Grading scale:
Point scale
Grading rules:
Internal examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
50No1 Week(s)Group (2 - 3)assignments
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA65611
Grading scale:
Point scale
Grading rules:
Internal and external examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
50No6 Hour(s)Individual take-home exam
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:50
Invigilation:No
Grouping (size):Group (2-3)
Duration:1 Week(s)
Comment:assignments
Exam code: GRA65611
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:No
Grouping (size):Individual
Duration:6 Hour(s)
Comment:take-home exam
Exam code: GRA65611
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