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Course Offerings

BACHELOR'S PROGRAM 

 

Lecture: Empirical Finance (6 ECTS, 4 SWS)

The aim of this course is to introduce the student to empirical data work in financial economics and investments. Students will learn and implement modern portfolio theory and the most important concepts to estimate expected returns and volatility. At the core of this lecture is the work on modern portfolio theory of Markowitz. Students will learn how to allocate investment opportunities to an optimal portfolio under investment constraints. To obtain the necessary inputs to this framework, students will revisit statistical concepts such as linear regression and maximum likelihood estimation to estimate expected returns and volatilities with econometric time series models.

Praktikum: Python for Empirical Finance (3 ECTS, 2 SWS)

The aim of this course is to provide students with strong knowledge in Python to independently solve real-world data problems related to computational risk and asset management. The course introduces students to Python, one of the most popular high-level programming languages in data analytics. After an introduction to the basic concepts, students will soon begin to solve problems related to the agenda of the lecture 'Empirical Finance'. This enables them to work with financial data, perform various statistical analysis and estimate their own time series models.

 

Seminar: Seminar in Empirical Finance (3 ECTS, 2 SWS)

The aim of this seminar is to introduce the student to empirical data work in financial economics and investments.

 

 

MASTER'S PROGRAM

 

Lecture: Computational Risk and Asset Management (6 ECTS, 3 SWS)

The aim of this course is to master real-world challenges of computational risk and asset management and provide students with a skill set to incorporate different portfolio objectives into the investment process. It enables students to solve such challenges independently in Python. Students will build up on the statistics and finance knowledge from their Bachelors program to learn about how to automatize modern quantitative portfolio strategies. Students learn about advanced topics which are relevant for a realistic, real-world asset and risk management process.

Praktikum: Python for Computational Risk and Asset Management (3 ECTS, 2 SWS)

The aim of this course is to provide students with strong knowledge in Python to independently solve real-world data problems related to automated robo investment advisory. The course introduces students to Python. Students will solve problems related to the agenda of the lecture 'Computational Risk and Asset Management'. This enables them to work with financial data, perform various statistical analysis and estimate their own time series models.

 

Praktikum: Engineering FinTech Solutions (9 ECTS, 6 SWS)

This project invites students to either pursue their own FinTech innovation project or to contribute to the Chair's ongoing innovation projects. Students will learn to connect innovative financial research with modern information technology to build a prototype that solves some daunting tasks for professional end-users in the field of modern asset and risk management. The course is targeted to students with strong knowledge in the field of computational risk and asset management and strong programming skills. It offers students the opportunity to develop an algorithmic solution and hence ample their programming experience and their understanding of financial economics or asset and risk management.

In order to take the course "Engineering FinTech Solutions", students must have completed the module "Data Science for Finance" with a grade of 1.3 or better.

 

Seminar: Seminar in Data Science for Finance (3 ECTS, 2 SWS)

The aim of this seminar is to master real-world challenges of computational risk and asset management. The CRAM team offers a wide range of topics across different asset classes and different stages of the investment process.