Optimization methods have a significant role in quantitative financial modeling. Many computational problems in finance can be solved by optimization techniques. This course will introduce the basics of optimization methods to solve many finance-related problems ranging from asset allocation to risk management, from option pricing to interest rate modeling. The main goal of this course is to become familiar with the basic optimization techniques and to apply them into various finance-related problems.
Language of instruction
|Thursday||03/17/22||10:00 AM - 01:00 PM||TC.3.10|
|Thursday||03/24/22||10:00 AM - 01:00 PM||TC.3.10|
|Thursday||03/31/22||10:00 AM - 01:00 PM||TC.3.10|
|Thursday||04/07/22||10:00 AM - 01:00 PM||D5.1.004|
|Thursday||04/28/22||10:00 AM - 12:00 PM||TC.4.27|
|Thursday||05/05/22||10:00 AM - 01:00 PM||TC.3.10|
|Thursday||05/12/22||10:00 AM - 01:00 PM||TC.3.10|
|Thursday||05/19/22||10:00 AM - 01:00 PM||TC.3.10|
After completing this course, the student will have the ability to
- understand the basics of optimization methods used in financial problems;
- apply optimization methods to concrete problems in the financial industry;
- learn how to solve optimization problems with the help of software, e.g., MATLAB, Excel Solver, Lindo or R.
There is mandatory on-site attendance. This means that students should attend at least 80% of all lectures (at most one session can be missed). Students are expected to be active in the class. Moreover, students will take part in a group work while working on the homework assignments and final project.
This course is mainly taught using a combination of (i) lectures elaborating relevant topics and (ii) examples (cases) illustrating and deepening various aspects of a specific topic. Real-world examples will allow students to apply theoretical knowledge to practical problems. Homework assignments and the final project will help students to consolidate and expand their knowledge and to understand the subject matter by developing solutions to applied problems. Furthermore, for the implementation and solution of the complex optimization problems, several programming languages will be presented and practiced.
The assessment is based on a midterm (40%), homework assignments (group work) (20%) and a final project ( group work) (40%). The following grading scale applies:
- 90.00-100.00 - Excellent (1)
- 80.00-89.00 - Good (2)
- 70.00-79.00 - Satisfactory (3)
- 60.00-69.00 - Sufficient (4)
- 00.00-59.00 - Insufficient (5)
Registration via LPIS
Sound knowledge in finance is necessary. Strong technical background (in mathematics and statistics) is an advantage.