2042 S3QM1 Quantitative Methods I
Assist.Prof. Priv.Doz.Dr. Paul Eisenberg
Contact details
Weekly hours
Language of instruction
09/02/22 to 09/25/22
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Day Date Time Room
Friday 10/14/22 10:00 AM - 01:30 PM D4.0.019
Friday 10/21/22 10:00 AM - 01:30 PM D4.0.019
Friday 10/28/22 10:00 AM - 01:30 PM D4.0.019
Friday 11/04/22 10:00 AM - 01:30 PM D4.0.019
Friday 11/11/22 10:00 AM - 01:30 PM D4.0.019
Friday 11/18/22 10:00 AM - 01:30 PM D4.0.019
Friday 11/25/22 10:00 AM - 01:30 PM D4.0.019
Friday 12/02/22 10:00 AM - 01:30 PM D4.0.022

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.

Learning outcomes

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.

Attendance requirements

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).

Teaching/learning method(s)

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. 


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Recommended previous knowledge and skills

Sound knowledge in derivative instruments is necessary. Strong background in mathematics and R programming is an advantage.

Last edited: 2022-05-02