ELE 3911 Introduction to Quantitative Finance
ELE 3911 Introduction to Quantitative Finance
Quantitative finance is an exciting field where we apply mathematics, statistics, and computing to solve financial problems. The main finance areas where advanced quantitative techniques are applied are derivative securities (pricing and hedging), risk management, and portfolio management. This course introduces the fundamental mathematical and statistical tools of quantitative finance in a rigorous way and with applications. The core aims are the analysis of financial variables, the modeling of uncertainty and risk, and the building of market models for pricing and portfolio choices.
The students by the end of the course will know:
- The fundamental probability theory and statistics underlying the modelling of uncertainty in finance
- The fundamentals of matrix algebra and vector spaces as used for modelling multivariate structures or series
- The theory of linear regression analysis
- Models of financial time series
- Market models and the notion of no arbitrage and replication
The students by the end of the course will be able to
- Model the time-value of money and analytically compute present and future values
- Model uncertainty in financial markets using various models and approaches
- Simulate univariate and multivariate financial time series using Monte Carlo simulations
- Construct discrete-time market models for pricing derivative securities
The students by the end of the course will be able to choose, analyze rigorously, and implement appropriate models of financial variables, depending on the problem at hand.
Subject to time constraints, the course will cover the following topics:
- A review of basic mathematics, including probability theory, statistics, linear algebra, and calculus.
- The time-value of money.
- Probability distributions.
- Bayesian analysis.
- Hypothesis testing.
- Linear algebra and vector spaces.
- Regression analysis.
- Time series models.
- Safe and risky assets.
- Discrete time market models.
The course contains lecturing, solving problems in class, online exercises, and a group project in R.
The course can be used as a preparation course for the Master's program.
Higher Education Entrance Qualification
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
Basic courses in Mathematics, Statistics and Finance.
| Assessments |
|---|
Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Weight: 40 Grouping: Group/Individual (1 - 2) Duration: 1 Semester(s) Comment: Assignment with data analysis and model implementation in R. Exam code: ELE 39112 Grading scale: ECTS Resit: Examination when next scheduled course |
Exam category: School Exam Form of assessment: Structured Test Exam/hand-in semester: First Semester Weight: 60 Grouping: Individual Support materials:
Duration: 4 Hour(s) Comment: Individual final exam. Exam code: ELE 39113 Grading scale: ECTS Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this course.
| Activity | Duration | Comment |
|---|---|---|
Teaching | 30 Hour(s) | |
Digital resources | 15 Hour(s) | Student's own work with learning platform material |
Student's own work with learning resources | 100 Hour(s) | |
Group work / Assignments | 51 Hour(s) | |
Examination | 4 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.