In case of limited activity on campus, the course will take place via Distance Mode.
There will be virtual synchronous course units and/or lecture casting under the' distance teaching' mode. Synchronous course units will mostly be devoted to working on examples as well as for Q&A.
Students are expected to be active online Q&A sessions as well as on the forum page of the course.
Online attendance is compulsory. The assessment is based on an online midterm (40%), homework assignments (group or individual work) (20%), and a final project (group or individual work) (40%).
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.
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.
Full attendance is mandatory. This means that students should attend at least 80% of all lectures ( at most one session can be missed).
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 (20%), and a final project (group or individual work) (40%). The following grading scale applies:
- 90.00-100.00 - Excellent (1)
- 80.00-89.99 - Good (2)
- 70.00-79.99 - Satisfactory (3)
- 60.00-69.99 - Sufficient (4)
- 00.00-59.99 - Insufficient (5)
Fulfillment of the specific requirements for admission to courses and examinations defined in the curriculum.
Sound knowledge in finance is necessary. Strong technical background (in mathematics and statistics) is an advantage.