GRA 6561 Advanced Computational Methods
GRA 6561 Advanced Computational Methods
This course covers advanced computational methods and their applications in finance and economics. These methods can be used for derivative pricing, asset pricing, macroeconomic simulation, and other pertinent applications in the industry, finance/economics academia, and central banks.
After taking the course, students should know:
- General numerical methods in economics and finance, such as nonlinear equation solving, optimization, approximation, finite difference, projection, and dynamic programming.
- Specialized numerical methods in derivatives pricing, such as lattice/tree methods, Monte Carlo methods, and PDE methods.
After taking the course, students should be able to:
- Apply advanced computational methods in finance and economics.
- Do programming in Matlab or similar programming languages.
- Understanding of quantitative models and ability to implement quantitative methods.
- General numerical methods
- Lattice/Tree method
- Monte Carlo method
- PDE approach in option pricing
- Dynamic programming
- Ethics and sustainability in quantitative finance
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.
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. For questions regarding previous results, please contact InfoHub.
It is the student’s own responsibility to obtain any information provided in class.
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.
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 |
|---|
Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Weight: 30 Grouping: Individual Duration: 1 Week(s) Comment: Home examination Exam code: GRA 65612 Grading scale: ECTS Resit: Examination when next scheduled course |
Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Weight: 70 Grouping: Individual Duration: 6 Hour(s) Comment: Home examination Exam code: GRA 65613 Grading scale: ECTS Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this course.
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.