GRA 6518 Fundamentals of Quantitative Finance
GRA 6518 Fundamentals of Quantitative Finance
This course covers the fundamental econometric and numerical tools used by quantitative analysts with a focus on programming and implementation. It starts with discussing fundamental concepts and econometric analysis of financial time series and model estimation. It then moves to review calculus and linear algebra to then cover a number of methods in numerical analysis; solution of linear and nonlinear systems of equations, least squares, interpolation and approximation of functions as well as numerical differentiation and integration.
By the end of the course, the students are expected to know:
- Linear and non-linear regressions with least squares
- The method of moments
- The basic idea behind maximum likelihood estimation
- Instrumental variables estimation
- The Generalized method of moments (GMM)
- How to interpolate and approximate functions
- Basic and more advanced methods optimization (constrained vs. unconstrained, one-dimensional vs. multi-dimensional, …)
- Numerical differentiation and numerical integration
By the end of the course, the students should have developed further the following key skills:
- written communication,
- oral communication,
- ethical awareness in conducting research,
- teamwork,
- problem solving and analysis,
- using initiative, and
- computer literacy.
The students by the end of the course are expected to be able to reflect on the workings and limitations of the different econometric and numerical tools.
- Introduction: The toolbox of a quantitative analyst
- Statistics and Econometrics
- Least squares estimation and method of moments
- Maximum likelihood estimation (MLE)
- Instrumental variables estimation
- Generalized method of moments (GMM)
- Numerical analysis
- Numerical analysis in a nutshell
- Linear equations and least square problems
- Basic methods of optimization
- Heuristic methods of optimization in a nutshell
- Solving linear and non-linear systems
- Interpolation and approximation of functions
- Numerical differentiation and integration
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 the course homepage/itslearning or 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 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. The expected behaviour and honour code is outlined here.
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.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 50 Grouping: Group/Individual (1 - 3) Duration: 1 Week(s) Comment: Assignment Exam code: GRA65181 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:
Duration: 2 Hour(s) Comment: Final written examination under supervision. Exam code: GRA65181 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
| Activity | Duration | Comment |
|---|---|---|
Teaching | 36 Hour(s) | |
Prepare for teaching | 50 Hour(s) | |
Submission(s) | 40 Hour(s) | |
Student's own work with learning resources | 34 Hour(s) |
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.