Registration via LPIS
|Tuesday||03/07/23||10:30 AM - 02:30 PM||TC.5.02|
|Tuesday||03/14/23||10:30 AM - 02:30 PM||TC.5.02|
|Tuesday||03/21/23||10:30 AM - 02:30 PM||TC.5.02|
|Tuesday||03/28/23||10:30 AM - 02:30 PM||TC.5.02|
|Tuesday||04/11/23||10:30 AM - 02:30 PM||TC.5.02|
|Tuesday||04/18/23||10:30 AM - 02:30 PM||TC.5.02|
|Tuesday||04/25/23||10:30 AM - 02:30 PM||TC.5.03|
During this course students will become acquainted with the essential techniques and tools for financial engineering. In particular, at the end of this course, students are expected to gain knowledge in:
- principles of Monte Carlo simulation;
- simulation of stochastic processes;
- variance reduction techniques;
- applications in derivative pricing and term-structure modelling;
- applications in risk management.
After completing this course the student will have the ability to:
- price derivatives using Monte Carlo techniques;
- apply variance reduction techniques ;
- simulate Brownian paths and SDEs;
- price exotic options by means of Monte Carlo simulation;
- construct a yield curve;
- perform basic risk analysis.
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 (exercises) illustrating and deepening various aspects of a certain topic. The lectures are aimed at providing the core information about the principles of financial engineering. The examples should give students the opportunity to apply theoretical knowledge to practical problems and help to comprehend the key ideas of the lecture. Regular homework assignments will help students to consolidate and expand their knowledge and understanding by developing solutions to applied problems.
- 30% Midterm
- 40% Final Exam
- 30% Homework assignment:
Homework assignments consist of practical problems to be solved on the computer. Assignments can be worked out in teams. The assessment of homework assignments will be based on the correctness of the results, the clarity of the work and the recognizable effort made. The late homework assignments will be penalized severely.
50% must be achieved in total to successfully pass the course.
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- Basic knowledge in linear algebra
- Basic knowledge in analysis
- Knowledge in stochastics
- Basic knowledge in finance
- Knowledge of a programming language