The course gives an introduction to the mathematical and computational techniques needed for quantitative finance.
The course has the following three parts.
1) Introduction to probability: Basics of probability, review of common distributions in finance, CLT, SLLN, basics of R programming.
2) Discrete time finance: Binomial tree-based models, risk-neutral valuation, option pricing, applications in R.
3) Basics of continuous time finance: Wiener processes and Ito's lemma, Black-Scholes-Merton Model for option pricing, Greeks, basics of Monte Carlo, applications in R.
After completing the lecture, the participants will be familiar with basic concepts in discrete and continuous time finance as well as basic applications in R.
There is mandatory on site (or online) attendance. This means that students should attend (online or on-site) at least 80% of all lectures ( at most one session can be missed).
This course is mainly taught using a combination of lectures elaborating on relevant theory and covering examples deepening various aspects of a specific topic. The students will have the chance to extend their understanding by the help of weekly homework assignments.
Homework assignments (group work, 30%), Final project (group work, 20%), Final Exam (50%).
For passing the course students will have to reach an overall score of at least 50% and a minimum score of 40% in the final exam.
Sound knowledge in derivative instruments is necessary. Strong background in mathematics and R programming is an advantage.