Area of specialization Computational Finance
Contact:
Ch. Schwab
Please comment on your experience with these courses
below under the corresponding headings. Always specify the semester and the name of the lecturer. In particular mention what you have perceived as prerequisites and dependencies.
General Remarks
For Bachelor students, this specialization can be very challenging and frustrating. You have to take the courses "Mathematical Foundations for Finance" and "Numerical Analysis of Stochastic Ordinary Differential Equations (Computational Methods for Quantitative Finance - Monte Carlo and Sampling Methods)". The latter one includes some nice programming in Matlab but the former one is a pure theoretical course with a lot of proofs that require more knowledge in probability theory than you probably have. If you want to choose this field of specialization, you should rather do it in your Master studies and just choose some other minor specialization for your Bachelor.
Specific Remarks on Courses
Mathematical Foundations for Finance
Autumn semester 2020:
(Lecturer: M. Schweizer)
Make sure that you either choose an elective course / core course that includes probability theory or that you take a similar course in this specialization like "Financial Engineering". This will make it much easier to follow this course. This course is very theoretical and thus can be very dry. If you like to proof abstract things, then you will surely enjoy it. But if you're not that familiar with expectations, variance or just don't like to proof statements, you might have a hard time here. Master students can take similar courses instead or just go completely in the computational direction.
This course gives a very short crash course about all the basics you need but it is highly advised that you're already familiar with expectations and their properties, as well as stochastic processes and martingales. Otherwise you might feel overwhelmed after the first two weeks.
Computational Methods for Quantitative Finance - Monte Carlo and Sampling Methods
Autumn semester 2020:
(Lecturer: D. Salimova)
This course combines theory, Matlab programming and applications and focuses on Stochastic (Ordinary) Differential Equations (SDE). You should know about basic probability theory such as expectations and variance already before taking this course. If you've never heard of SDEs, you don't have to worry too much. They will get nicely introduced during the course and you will do a lot of examples (both programming and on paper) for different kind of SDEs. The exam is computer-based and you have to implement certain methods in Matlab. Skeleton codes are given in the exam and in most programming exercises and are usually not too complicated. Just note that the exam is not a pure programming exam but also requires to calculate things on paper and apply short theorems from the course.
Financial Engineering
Financial Market Risks
Computational Methods for Financial Market Risks
Quantitative Finance - PDE methods
Machine Learning in Finance
Continuous Time Quantitative Finance
Seminar in Comput. Finance for CSE
