GRA 6515 Quantitative Methods for Finance
GRA 6515 Quantitative Methods for Finance
This course lays a foundation for the understanding of quantitative methods most commonly used in finance worldwide. These include statistics, probability theory, hypothesis testing, regression analysis, and simulation methods. This course also introduces R programming, an essential programming language that will be used in the rest of the program.
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
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 in R with the emphasis on data processing, statistical analysis, and simulation of economic processes
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 R.
Part I: Review Topics
- Probability theory
- Descriptive statistics
- Sampling and estimation
- Hypothesis testing
Part II: Regression analysis
- Simple linear regression
- Multivariate regression
- Issues in regression analysis
Part III: Modelling uncertainty with R
- Modelling uncertainty in financial markets
- Monte Carlo simulations
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 R. This allows the students to structure the data analysis tasks into smaller consecutive steps and develop the algorithms that execute these steps. Here, strong emphasis will be placed on statistical and economic interpretations of the results. Since mastering a programming language requires practice, 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. Finally, the course includes a graded mid-term exam that provides the students with an interim assessment of their standing.
R will be used as the main analytical tool. Prior knowledge of R is not required before the course starts but students are encouraged to attend introductory lectures on R and install the required software before the course start.
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 cutoff points with reference to the letter grades when the course starts.
At resit, all exam components must, as a main rule, be retaken during the 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.
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 |
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Exam category: Submission Form of assessment: Written submission Weight: 20 Grouping: Group/Individual (1 - 3) Duration: 1 Semester(s) 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:
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:
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 |
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.
Group work (individual work is an exception).